23 jobs found

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a MLOps Engineer based in United Kingdom. Join a high-impact engineering team building the infrastructure that powers next-generation AI solutions for enterprise-scale decision-making. In this role, you will design, deploy, and optimize production-grade machine learning systems that support the full ML lifecycle, from training to inference. You'll collaborate with talented engineers to create highly scalable, reliable, and secure MLOps platforms capable of handling demanding workloads. This is an opportunity to solve complex technical challenges, improve model performance at scale, and contribute to cutting-edge AI technologies in a fast-paced, collaborative, and remote-first environment. The role offers significant ownership, modern cloud-native tooling, and the chance to shape the future of production AI systems. Accountabilities Develop, automate, and maintain scalable machine learning pipelines, CI/CD workflows, and orchestration frameworks to support efficient model development and deployment. Design and implement high-performance model serving infrastructure using industry-standard serving frameworks while optimizing inference for low latency and high throughput. Build reliable deployment strategies including A/B testing, canary releases, rollback mechanisms, and production validation processes. Create robust monitoring, logging, alerting, and observability solutions to ensure model reliability, performance, and operational excellence. Optimize infrastructure utilization by improving GPU efficiency, enabling autoscaling, and managing cloud resources effectively. Design and maintain feature stores, scalable data pipelines, and storage architectures capable of supporting large-scale training and inference workloads. Collaborate with engineering teams to continuously improve platform scalability, security, governance, and operational best practices. Requirements Bachelor's or Master's degree in Computer Science, Engineering, or a related discipline, or equivalent practical experience. At least 5 years of experience in MLOps, DevOps, or related engineering roles supporting production machine learning environments. Proven experience designing and building MLOps infrastructure from the ground up using platforms such as MLflow, Weights & Biases, Kubeflow, or similar. Strong hands on experience with machine learning frameworks including PyTorch and TensorFlow, as well as model serving technologies such as TorchServe, TensorFlow Serving, Triton, or KServe. Solid experience developing and managing scalable data pipelines, Kubernetes environments, cloud infrastructure (AWS, GCP, or Azure), and Infrastructure as Code solutions including Terraform, Helm, or GitOps. Strong programming skills in Python, Bash, and Go, with a focus on maintainable, scalable, and production quality software. Knowledge of AI system security, model governance, compliance, monitoring, and observability tools such as Prometheus, Grafana, Datadog, or OpenTelemetry. Experience with FastAPI, Databricks, Snowflake, SRE practices, or cloud security certifications is considered an advantage. Benefits Competitive salary and equity package. Comprehensive healthcare coverage for employees and eligible dependents. Paid parental leave supporting all paths to parenthood, including adoption and surrogacy. Relocation assistance for employees joining one of the company's office locations where applicable. Fully remote work within Europe. Opportunity to work on cutting edge AI technologies with significant technical ownership. Inclusive, collaborative, and mission driven engineering culture focused on innovation, learning, and professional growth.

HypervisionSurgical("Hypervision")is a spin-out from King's College London, founded by clinicians and experts in medical imaging and artificial intelligence. Using safe light alone, our mission is to equip surgeons with real-time, AI-driven tissue intelligence to improve precision and patient safety. We are pioneering the world's first regulatory-cleared real-time intraoperative spectral imaging platform, combining on-chip spectral sensing with high-speed AI analytics at over 60 frames per second. Seamlessly integrating into existing surgical vision systems, our technology transforms standard cameras into intelligent, data-rich tools, revealing anatomical, physiological, and pathological information beyond human vision. Certified for both open and minimally invasive surgery, our platform achievedUKCA certificationandFDA clearancein 2025 under a newly established AI/ML productcode, andwas admitted into the FDA's Safer Technology Program. With multi-centre clinical evaluations underway and strategic partnershipswith world-leading technology and surgical manufactures,includingimecandZEISS Ventures,Hypervisionis shaping the future of data-driven surgery. Hypervision Surgical process all personal data in accordance with the UK GDPR and Data Protection Act 2018. For further information on how we collect, use and protect your data, please refer to our Applicant Privacy Notice. The role We are seeking an experienced Senior Machine Learning Engineer to support the development and deployment of our AI/ML surgical vision platform, taking algorithms from research prototype to production deployment, and building the data pipelines that turn our hyperspectral imaging system into a continuously improving clinical tool. As a Senior Machine Learning Engineer, you will work alongside our research scientists, engineers, and clinical development team to shape both the algorithms and the platform that delivers them. In particular, you will contribute architecturally and in a hands-on capacity to the design, training, evaluation, and production deployment of machine learning models for hyperspectral image processing, including image reconstruction, tissue characterisation, and semantic segmentation design and build the data pipelines that turn raw clinical recordings into structured, governed training datasets, supporting continuous training, model improvement, and re-validation cycles architect and operate the production ML stack, including model versioning, deployment, monitoring, drift detection, and rollback, for our cloud-enabled, regulatory-cleared surgical imaging platform establish and maintain MLOps best practices, including reproducible training, dataset governance, experiment tracking, model documentation, that scale across multiple algorithms, sensors, and clinical indications mentor more junior research scientists and engineers identify and surface novel features in support of patenting activities work closely with our software development and regulatory team for efficient integration from R&D to deployment At Hypervision Surgical, we welcome candidates who have the core skills for the post and are keen to learn and grow with us. We are committed to creating an inclusive environment where a diverse mix of talented people come and enjoy working with each other. By working together, we will change the way surgery is performed and improve patient care. A bitaboutyou PhD or MSc in Machine Learning, Physics, Mathematics, Computer Vision, or related technical discipline 6+ years industry experience designing, training, evaluating, and deploying machine learning models, ideally for vision applications in a regulated medical context Demonstrated track record of taking ML systems from research prototype to production deployment at scale Strong experience building and maintaining data pipelines for continuous training, with a focus on reproducibility, dataset versioning, and efficient access Working knowledge of cloud platforms (AWS, GCP, or Azure) and modern MLOps tooling (e.g. MLflow, Weights & Biases, DVC, Airflow) Strong experience with Python and associated scientific software packages such as PyTorch, OpenCV, Pandas, SciPy, NumPy, SciKit-learn, etc. Strong software engineering practices including version control, code review, software testing methodologies, and continuous integration; experience with IEC 62304 is particularly desirable Excellent oral and written communication skills, and comfort working at the interface between research, engineering, regulatory, and clinical teams Experience mentoring or leading junior engineers or scientists Analytical thinker, attentive to details, creative and team player Bonus points if you bring a special talent, interest, new language, or unique life experience to the team. What we offer The opportunity to make a direct contribution to patient care and deliver real-world surgical impact Access to state-of-the-art surgical development facilities at St Thomas' MedTech Hub, including hospitals, operating rooms, labs, and computational resources, with offices located at the London Institute for Healthcare Engineering Equity participation via share option scheme 25 days of annual leave plus bank holidays Hybrid working arrangements, tailored with your manager to suit the needs of the role Employee Assistance Programme for wellbeing, legal, and financial support Cycle to Work Scheme and Workplace Nursery Benefits £150 annual tech stipend for productivity and office essentials Complimentary office snacks and drinks Monthly team socials in an inclusive, collaborative culture

United States Digital Space LLC in London seeks a Software Engineer for its GenAI Platform to build production infrastructure for Generative AI at scale, focusing on the open-weights model platform, real-time GPU serving, and fine-tuning pipelines. You will work across model serving, inference engines, GPUs, and observability to deliver low-latency endpoints and cost-efficient systems. Collaboration with ML engineers, data scientists, and platform teams across partners is key.

Software Engineer, GenAI Platform About the Team the company's GenAI Platform team sits within Machine Learning Platform and builds the shared infrastructure that helps DoorDash, Wolt, and the company teams safely bring GenAI-powered products, agents, automation, and personalization to production. Our mission is to increase the velocity of business impact from GenAI. A central pillar of that work is running frontier open-weight LLMs and VLMs (such as GLM, Qwen, Kimi, and DeepSeek) ourselves - real-time GPU serving, high-throughput batch inference, and fine-tuning on autoscaling GPUs - delivering large cost and latency wins (for example, a billion embeddings produced roughly 20 cheaper and visual models served roughly 72% cheaper). We also own core platform surfaces including the LLM Gateway, Agent Gateway, evals infrastructure, guardrails, and cost attribution. About the Role You will join a small, high-leverage team building production infrastructure for Generative AI at the company and DoorDash, with a primary focus on our open-weights model platform spanning inference and fine-tuning: real-time GPU serving, high-throughput batch inference, and model fine-tuning. You'll work across model serving and inference engines, fine-tuning and training pipelines, GPU autoscaling and utilization, batch pipelines, backend services, and observability. This role is ideal for an engineer who enjoys pushing the cost/performance frontier of GPU inference and fine-tuning in a fast-moving technical area where product needs, model capabilities, vendor ecosystems, and cost/performance tradeoffs are evolving quickly. You're excited about this opportunity because you will Build the infrastructure that helps the company teams move GenAI ideas from prototype to production, increasing the velocity of business impact from AI across the company. Work on our open-weights serving stack - real-time GPU endpoints, high-throughput batch inference, and fine-tuning (SFT/DPO/LoRA) - alongside the LLM Gateway, Agent Gateway, evals infrastructure, guardrails, and cost attribution. Design scalable, high-performance systems for model serving, batch inference, GPU autoscaling, and fine-tuning that power real customer and internal automation use cases Push the cost and latency frontier of GPU inference - turning batch jobs that took days into hours and cutting inference cost by multiples - while giving product teams a clean choice across open-weight and closed-source models with reliability, fallback, observability, and cost controls built in. Build platforms that support rapid experimentation while meeting production standards for latency, scale, monitoring, SLOs, playbooks, and operational excellence. Partner closely with ML engineers, product engineers, data scientists, and platform teams across DoorDash, Wolt, and the company to turn emerging GenAI capabilities into durable platform primitives. Shape the future of the centralized GenAI platform - including emerging directions such as reinforcement learning (RLHF/RLVR), agent optimization, and other post-training and agentic techniques - enabling the next generation of AI-powered products, agents, automation, and personalization. We're excited about you because you have BSc, MSc, or PhD in Computer Science or equivalent 3+ years of industry experience in software engineering Strong backend engineering fundamentals, especially in Python and distributed systems. Experience building production services, APIs, data pipelines, or ML infrastructure at scale. Experience operating systems in production, including observability, debugging, reliability, incident response, and performance/cost optimization. Hands on experience with LLM inference and/or fine tuning of open weight models in production - serving (latency, throughput, batching, autoscaling, GPU utilization) and/or fine tuning (SFT/DPO/LoRA). Ability to work across ambiguous, fast moving technical areas and turn customer use cases into reusable platform capabilities Proficiency in using AI coding tools (e.g., Claude Code, Codex, Cursor) in the full software development lifecycle, including designing, generating code, testing, monitoring and releasing software Nice To Haves Experience with LLM inference engines and serving frameworks (e.g., vLLM, SGLang, TensorRT LLM) in production Experience with distributed/multi node fine tuning and training pipelines (SFT, DPO/RLHF, LoRA), including data preparation and evaluation GPU performance work - multi node/distributed inference, KV cache/memory optimization, quantization (FP8/INT8/AWQ/GPTQ), or cold start/throughput tuning Experience with Kubernetes, cloud infrastructure (AWS/GCP), GPUs, serverless/elastic GPU platforms (e.g., Modal), or high throughput batch systems Experience with LLM gateways, model routing, vendor abstraction, or cost attribution Experience building developer platforms, internal platforms, or self serve infrastructure Experience building and deploying AI agents or MCP servers in production Experience with eval systems, LLM observability, tracing, RAG, search, or vector databases Diversity, Equity and Inclusion At the company, we know that a great workplace reflects the world around us and that true diversity and inclusion make us stronger, more creative, and better at what we do. We're committed to fostering an environment where everyone can do their best work and feel they belong. We believe in equality of opportunity and welcome candidates from all backgrounds regardless of age, gender, ethnicity, disability, sexual orientation, gender identity, socio economic background, religion, or belief. If you have a disability or long term health condition and need support to apply for one of our roles, or require any reasonable adjustments during the recruitment process, you'll have the opportunity to let us know once you've submitted your application. We'll share details on how to request support so we can ensure you have a fair and equitable experience. If you're excited about making a real impact in a fast moving marketplace and growing your career alongside ambitious, supportive teams, we'd love to hear from you!

Software Engineer, Machine Learning Infrastructure - Generative AI About the Team the company's GenAI Platform team sits within Machine Learning Platform and builds the shared infrastructure that helps DoorDash, Wolt, and the company teams safely bring GenAI-powered products, agents, automation, and personalization to production. Our mission is to increase the velocity of business impact from GenAI. A central pillar of that work is running frontier open-weight LLMs and VLMs (such as GLM, Qwen, Kimi, and DeepSeek) ourselves - real-time GPU serving, high-throughput batch inference, and fine-tuning on autoscaling GPUs - delivering large cost and latency wins (for example, a billion embeddings produced roughly 20x cheaper and visual models served roughly 72% cheaper). We also own core platform surfaces including the LLM Gateway, Agent Gateway, evals infrastructure, guardrails, and cost attribution. About the Role You will join a small, high-leverage team building production infrastructure for Generative AI at the company and DoorDash, with a primary focus on our open-weights model platform spanning inference and fine-tuning: real-time GPU serving, high-throughput batch inference, and model fine-tuning. You'll work across model serving and inference engines, fine-tuning and training pipelines, GPU autoscaling and utilization, batch pipelines, backend services, and observability. This role is ideal for an engineer who enjoys pushing the cost/performance frontier of GPU inference and fine-tuning in a fast-moving technical area where product needs, model capabilities, vendor ecosystems, and cost/performance tradeoffs are evolving quickly. You're excited about this opportunity because you will Build the infrastructure that helps the company teams move GenAI ideas from prototype to production, increasing the velocity of business impact from AI across the company. Work on our open-weights serving stack - real-time GPU endpoints, high-throughput batch inference, and fine-tuning (SFT/DPO/LoRA) - alongside the LLM Gateway, Agent Gateway, evals infrastructure, guardrails, and cost attribution. Design scalable, high-performance systems for model serving, batch inference, GPU autoscaling, and fine-tuning that power real customer and internal automation use cases Push the cost and latency frontier of GPU inference - turning batch jobs that took days into hours and cutting inference cost by multiples - while giving product teams a clean choice across open-weight and closed-source models with reliability, fallback, observability, and cost controls built in. Build platforms that support rapid experimentation while meeting production standards for latency, scale, monitoring, SLOs, playbooks, and operational excellence. Partner closely with ML engineers, product engineers, data scientists, and platform teams across DoorDash, Wolt, and the company to turn emerging GenAI capabilities into durable platform primitives. Shape the future of the centralized GenAI platform - including emerging directions such as reinforcement learning (RLHF/RLVR), agent optimization, and other post-training and agentic techniques - enabling the next generation of AI-powered products, agents, automation, and personalization. We're excited about you because you have BSc, MSc, or PhD in Computer Science or equivalent 3+ years of industry experience in software engineering Strong backend engineering fundamentals, especially in Python and distributed systems. Experience building production services, APIs, data pipelines, or ML infrastructure at scale. Experience operating systems in production, including observability, debugging, reliability, incident response, and performance/cost optimization. Hands on experience with LLM inference and/or fine tuning of open weight models in production - serving (latency, throughput, batching, autoscaling, GPU utilization) and/or fine tuning (SFT/DPO/LoRA). Ability to work across ambiguous, fast moving technical areas and turn customer use cases into reusable platform capabilities Proficiency in using AI coding tools (e.g., Claude Code, Codex, Cursor) in the full software development lifecycle, including designing, generating code, testing, monitoring and releasing software Nice To Haves Experience with LLM inference engines and serving frameworks (e.g., vLLM, SGLang, TensorRT-LLM) in production Experience with distributed/multi node fine tuning and training pipelines (SFT, DPO/RLHF, LoRA), including data preparation and evaluation GPU performance work - multi node/distributed inference, KV cache/memory optimization, quantization (FP8/INT8/AWQ/GPTQ), or cold start/throughput tuning Experience with Kubernetes, cloud infrastructure (AWS/GCP), GPUs, serverless/elastic GPU platforms (e.g., Modal), or high throughput batch systems Experience with LLM gateways, model routing, vendor abstraction, or cost attribution Experience building developer platforms, internal platforms, or self serve infrastructure Experience building and deploying AI agents or MCP servers in production Experience with eval systems, LLM observability, tracing, RAG, search, or vector databases Diversity, Equity and Inclusion At the company, we know that a great workplace reflects the world around us and that true diversity and inclusion make us stronger, more creative, and better at what we do. We're committed to fostering an environment where everyone can do their best work and feel they belong. We believe in equality of opportunity and welcome candidates from all backgrounds regardless of age, gender, ethnicity, disability, sexual orientation, gender identity, socio economic background, religion, or belief. If you have a disability or long term health condition and need support to apply for one of our roles, or require any reasonable adjustments during the recruitment process, you'll have the opportunity to let us know once you've submitted your application. We'll share details on how to request support so we can ensure you have a fair and equitable experience.

This is a very interesting project focused on pricing optimisation for insurance companies, using an ensemble of predictive models. The tech stack includes: pandas, numpy, PyTorch, SHAP, and scikit-learn. We are looking for candidates with experience in building predictive models for time series, strong attention to detail, and a continuous desire to improve and expand their machine learning skills. Responsibilities Create statistical summaries to support hypothesis testing and data-driven decisions during EDA Implement data preparation and feature engineering pipelines for the models Plan and implement algorithms for predictive modeling. Provide continuous improvement of models in production Lead the ML-driven python coding for end-to-end model deliveries, support our ML-related libraries/ Collaborate with a cross-functional team of specialists, including senior and junior data scientists, Python developers and ML engineers, data analysts, and DevOps specialists. Provide expertise in tuning loss functions, metrics, sample weights adjustment, hyperparameter tuning and model improvement in general. Generate detailed evaluation reports, summarizing key findings and presenting actionable insights to stakeholders. Keep up with tight deadlines, agile environment of work with evolving objectives and KPIs, having the highest level of organization and self management to provide full work clarity, extensive tracking and documentation of your work. Results / KPI Competitive remuneration package. Professional mentorship and guidance from experienced team members. Opportunities for professional growth and continuous learning. A dynamic and collaborative work environment. Requirements Proficiency in Python and machine learning. Experience with GitHub, Power BI, and Python tools. Familiarity with relevant data science libraries such as pandas, numpy, scikit-learn, and PyTorch. Strong analytical skills and attention to detail. Ability to work in an agile environment and meet tight deadlines. About Us DataObrii is a high-tech consulting firm specializing in data science, machine learning, and AI augmented Internet of Things. Our team comprises experienced data scientists, python engineers, devops, hardware electrical engineers, and business analysts dedicated to delivering innovative, data driven solutions that enhance business intelligence and efficiency. We emphasize continuous improvement, keeping up with current and emerging technologies, and delivering complete timely, effective solutions. Our Values: Efficiency - We employ an agile approach to ensure timely delivery of high-quality solutions. This governs fast delivery cycles, quick and efficient solutions, iteratively going from PoC developments to fully enhanced production systems. Professionalism - Our commitment to excellence drives us to achieve success for our clients. This includes both technical and ethical proficiency required from all our employees. Creativity - We utilize design thinking to develop innovative solutions that address complex business challenges. Care - We invest time in understanding our clients' business models to provide tailored solutions that align with their objectives.

Helical is building the in-silico labs for biology Drug discovery still relies on wet labs: slow, expensive, and constrained by physical trial-and-error. Helical is changing that. We build the application layer that makes Bio Foundation Models usable in real-world drug discovery, enabling pharma and biotech teams to run millions of virtual experiments in days, not years. Today, leading global pharma companies already use Helical, and we're at the start of a highly ambitious growth journey. We're a founder-led, talent-dense team building a category-defining company from Europe. We care deeply about the quality of our work, move fast, and expect ownership. If you're excited by complexity, real responsibility, and shaping how a company actually operates as it scales, you'll feel at home here. Your Role As a Machine Learning Engineer - Scaling at Helical, you'll build, optimize, and scale real-world applications of bio foundation models You'll work closely with researchers and product engineers to productionize model training, inference, and deployment workflows. You'll also help push the limits of foundation models by prototyping new methods, contributing to our core ML infrastructure, and translating research into fast, iterative code. This is a deeply technical role with high ownership - ideal for engineers who want to operate at the bleeding edge of AI infrastructure, model development, and system design. What You'll Do Build and maintain scalable training/inference pipelines for foundation models (e.g. Transformers, SSMs). Optimize model performance, latency, and throughput across environments. Design modular, reusable ML components for internal and open-source use. Collaborate with researchers to scale notebooks into production-grade systems. Own ML infrastructure components (data loading, distributed compute, experiment tracking, etc.). Essentials MSc or PhD in Machine Learning, Computer Science, Applied Math, or similar. Strong Python programming skills, with deep knowledge of PyTorch, JAX, or TensorFlow. Hands on experience building and scaling ML pipelines in real-world settings. Comfort with MLOps tools and practices (e.g. Weights & Biases, Ray, Docker, etc.). Experience with modern ML architectures - Transformers, Diffusion Models, SSMs, etc. High agency, fast iteration speed, and comfort with ambiguity in early stage environments Bonus Points Contributions to open source ML libraries or tooling. Experience with distributed training, model compression, or serving at scale. Scaling AI Systems For Large Post-Training Runs. Knowledge of how to integrate ML systems into user-facing applications or APIs. Interest in the biology/pharma space (not required, but you'll pick it up fast here!)

You will operate at the intersection of Data Engineering, Data Science, and modern AI/ML systems, taking ownership of initiatives that directly shape product and business outcomes. We need someone with genuine breadth - equally comfortable designing scalable data pipelines as they are building agentic AI architectures - and with the curiosity to keep pace with a space that is moving faster than almost any other in software engineering. You will bring deep technical expertise across the full AI/ML stack alongside the leadership qualities to mentor colleagues, challenge assumptions, and drive a culture of engineering excellence. Crucially, you will not just set direction - you will get your hands dirty and build it too. What You Will Be Doing This role spans two interconnected disciplines. We are looking for strong coverage across both. Lead the refactoring of legacy infrastructure into highly scalable, secure, and multi-tenant data pipelines that power our Security Portfolio Intelligence platform. Own data quality, governance, and security end-to-end: establishing robust validation frameworks, automated alerting, and compliance-ready data modeling for highly regulated enterprise clients. Champion pragmatic AI: ruthlessly identify where LLMs can be replaced by leaner, more cost-effective classical ML models, ensuring optimal performance and cost-efficiency. Evolve our data architecture: champion the appropriate pattern for the job, whether that's a GraphDB, vector stores, and more standard SQL/NoSQL structures, all whilst ensuring scalability and long-term maintainability. Establish and promote good data modelling practices across the organisation - schema design, query optimisation, and a sensible approach to data governance. Work across a range of storage paradigms: SQL (PostgreSQL, MySQL), vector databases (pgvector and equivalents), and graph databases (Neo4j or similar). Design and ship agentic AI pipelines and multi-agent reasoning systems that solve real business problems - content review, classification, enrichment, and beyond. Lead the evaluation and adoption of emerging AI/ML tooling: Vertex AI, Google ADK, AWS SageMaker, Azure ML, and next-generation LLM frameworks. Establish LLMOps practices: formal evaluation pipelines, regression testing, and quality baselines so we always know whether our AI systems are improving or declining. Identify where Large Language Models can be replaced by leaner, more cost-effective traditional ML models - and deliver those replacements. Build NLP-powered systems, including classifiers, semantic search, and potentially fine-tuned or custom-trained models where the use case justifies it. Drive the auto-generation of marketing content and other AI-powered product features, working closely with Product to turn ideas into production systems. Bring your own ideas to the table. If you see an opportunity we have not spotted, we want to hear it - and we will give you the space and support to explore it. Leadership & Cross-Cutting Responsibilities Mentor and collaborate with engineers across the team, raising the collective bar for AI/ML quality, reproducibility, and best practice. Run tech-sharing sessions; keep the team current on fast-moving developments in the AI/data space. Contribute to hiring: interview, assess, and help build the team you want to work in. Requirements (Must-Have) 7+ years of professional experience across Data Engineering, Data Science, or Machine Learning roles - with meaningful exposure to both the data and AI/ML sides of that spectrum. Hands-on experience designing and shipping agentic AI systems and multi-agent architectures (LangChain, LangGraph, AutoGen, Google ADK, or similar frameworks). Strong working knowledge of Large Language Models: prompt engineering, evaluation, and responsible deployment in production. Experience with Cloud ML platforms - at least one of Vertex AI (GCP), SageMaker (AWS), or Azure Machine Learning. Expertise in Python for data processing, model training, and API development. Solid understanding of classical ML and NLP: ability to identify when a simpler model outperforms an LLM in production and to deliver that alternative. Relational databases: PostgreSQL, MySQL, or equivalent - schema design, query optimisation, and data modelling. Vector databases: practical production experience with pgvector, Pinecone, Weaviate, or similar for semantic search and RAG pipelines. Graph databases: Neo4j or equivalent; experience modelling domain knowledge as a graph. Demonstrated technical leadership - not necessarily formal line management, but clear ownership of complex technical workstreams and influence over engineering decisions. Strong communication skills; comfortable translating technical concepts for non-technical stakeholders. Nice-to-Have (Bonus Points) Cybersecurity domain knowledge, specifically an understanding of how enterprise security tools map to frameworks like NIST and MITRE. GraphQL API design and implementation. MLOps / LLMOps tooling: MLflow, Weights & Biases, Evidently AI, or similar for experiment tracking and model monitoring. Experience training or fine-tuning your own models (transformer-based or otherwise) from scratch or from pre-trained checkpoints. NLP specialism: named entity recognition, text classification, semantic similarity, topic modelling, or conversational AI. Data orchestration tools: Airflow, Prefect, Dagster. Experience working with Knowledge Graphs or ontologies in a production environment. Published work, open-source contributions, or a track record of writing or speaking about AI/ML topics. Engineering Manchester, UK (UK-Based Remote Considered) Full-time

Helical is building the in-silico labs for biology Drug discovery still relies on wet labs: slow, expensive, and constrained by physical trial-and-error. Helical is changing that. We build the application layer that makes Bio Foundation Models usable in real-world drug discovery, enabling pharma and biotech teams to run millions of virtual experiments in days, not years. Today, leading global pharma companies already use Helical, and we're at the start of a highly ambitious growth journey. We're a founder-led, talent-dense team building a category-defining company from Europe. We care deeply about the quality of our work, move fast, and expect ownership. If you're excited by complexity, real responsibility, and shaping how a company actually operates as it scales, you'll feel at home here. Your Role As a Machine Learning Engineer - Scaling at Helical, you'll build, optimize, and scale real-world applications of bio foundation models You'll work closely with researchers and product engineers to productionize model training, inference, and deployment workflows. You'll also help push the limits of foundation models by prototyping new methods, contributing to our core ML infrastructure, and translating research into fast, iterative code. This is a deeply technical role with high ownership - ideal for engineers who want to operate at the bleeding edge of AI infrastructure, model development, and system design. What You'll Do Build and maintain scalable training/inference pipelines for foundation models (e.g. Transformers, SSMs). Optimize model performance, latency, and throughput across environments. Design modular, reusable ML components for internal and open-source use. Collaborate with researchers to scale notebooks into production-grade systems. Own ML infrastructure components (data loading, distributed compute, experiment tracking, etc.). Essentials MSc or PhD in Machine Learning, Computer Science, Applied Math, or similar. Strong Python programming skills, with deep knowledge of PyTorch, JAX, or TensorFlow. Hands on experience building and scaling ML pipelines in real-world settings. Comfort with MLOps tools and practices (e.g. Weights & Biases, Ray, Docker, etc.). Experience with modern ML architectures - Transformers, Diffusion Models, SSMs, etc. High agency, fast iteration speed, and comfort with ambiguity in early stage environments Bonus Points Contributions to open source ML libraries or tooling. Experience with distributed training, model compression, or serving at scale. Scaling AI Systems For Large Post-Training Runs. Knowledge of how to integrate ML systems into user-facing applications or APIs. Interest in the biology/pharma space (not required, but you'll pick it up fast here!)

Do you want to work on one of the hardest problems in AI, building systems that can reason and act in complex, open ended digital worlds? Do you want your work to ship quickly and sit at the core of a product used by major game studios, with a clear and visible impact on real games? And do you want genuine ownership, the freedom to choose the stack, design the architecture end to end, and take ideas from first principles through to production without layers of approval? About us We're an early-stage startup built on a simple belief: game developers should be building worlds, not chasing bugs. We're replacing manual QA with autonomous agents that truly understand gameplay. Our small, focused team combines deep machine learning research with strong commercial execution, driven to solve some of the hardest problems in game development. Just over a year in, we've built category-leading technology, gained real traction, and partnered with some of the world's most storied game studios. We're backed by top-tier investors and incubators, including SVV and EWOR. The role We are looking for a Founding AI Engineer to be the architect of our agents' cognition. You will be the builder who transforms raw video pixels into semantic game logic. You will own the entire model lifecycle, from curating specialized datasets to fine-tuning VLMs that run with extreme efficiency. This role demands a blend of research-grade intuition and rigorous engineering discipline. You are constructing the proprietary brain that gives our agents the agency to play. Key responsibilities Train the Brain Fine-tune and optimize Vision-Language Models (VLMs) on custom game footage, enabling our agents to interpret complex UI, animations, and temporal game states. Solve for Time Architect model inputs to handle video sequences rather than static frames, giving agents the memory and context required to understand gameplay progression. Deploy for Speed Optimize and deploy models to run with the latency and throughput real-time gameplay demands, balancing accuracy against inference cost. Define the Data Design and automate the data collection pipelines that turn raw gameplay into high-quality, annotated training sets for specific game genres. Define the Benchmarks Create and maintain internal eval suites that measure what actually matters. You're a good fit if you You have spent serious time fine tuning Vision Language Models, not just reading about them, and you are comfortable working with video data where understanding temporal context over time matters more than a single frame. You can take research ideas and turn them into systems that actually run in production, bridging the gap between experimentation and real world deployment. Live in the Weights You have hands-on experience training or fine-tuning multi-modal models (e.g., LLaVA, CLIP, Flamingo) from scratch or checkpoints. You know exactly what happens inside the transformer block. Build for the Real World You have successfully deployed deep learning models into production environments where latency and cost matter. You understand the brutal reality of trade-offs between model size and inference speed. Thrive in Chaos You are a self-starter who prefers the ambiguity of a zero-to-one startup environment over the structured comfort of a big tech research lab. You want your code to ship, not just be published. Bonus You enjoy games or complex interactive systems. You don't have to be a hardcore gamer, but you're genuinely curious about how interactive experiences are built, broken, and tested; and you like the idea of agents learning to navigate them. What's on offer High trust, high impact: ship real product fast, work directly with founders. Equity Meaningful equity ownership. You'll share in the upside you help create. Flexible hours, and a culture built on trust and output.

About WRITER WRITER is where the world's leading enterprises orchestrate AI-powered work. Our vision is to expand human capacity through superintelligence. And we're proving it's possible - through powerful, trustworthy AI that unites IT and business teams together to unlock enterprise-wide transformation. With WRITER's end-to-end platform, hundreds of companies like Mars, Marriott, Uber, and Vanguard are building and deploying AI agents that are grounded in their company's data and fueled by WRITER's enterprise-grade LLMs. Valued at $1.9B and backed by industry-leading investors including Premji Invest, Radical Ventures, and ICONIQ Growth, WRITER is rapidly cementing its position as the leader in enterprise generative AI. Founded in 2020 with office hubs in San Francisco, New York City, Austin, Chicago, and London, our team thinks big and moves fast, and we're looking for smart, hardworking builders and scalers to join us on our journey to create a better future of work with AI. About the role Join WRITER's security team as a staff detection and response engineer and help protect the AI infrastructure that's transforming how the world works. You'll build sophisticated detection systems that identify attacks targeting our AI platform, training data, and model deployments while creating automated response capabilities that scale with our explosive growth. This isn't just traditional security work - you're defending cutting-edge AI/AGI systems against adversaries who are evolving their tactics as fast as AI itself advances. This role combines hands on security engineering with strategic thinking to stay ahead of novel threats that don't exist in textbooks yet. You'll be the operational arm of our security function, translating threat intelligence into real time detections, coordinating incident response across multiple teams, and hunting for sophisticated attacks across GPU clusters and distributed training environments. If you're excited by the challenge of securing systems that are fundamentally different from anything you've protected before, this is your opportunity to define what AI security engineering looks like at scale. You'll work closely with our AI Security research team, Cloud Infrastructure, Software Security Engineering, and AI researchers to build a defense in depth strategy that protects one of the most valuable AI platforms in the industry. The threats are real, the stakes are high, and the problems are intellectually fascinating. This role can be based inSan reporting to our head of security operations. What you'll do Design and implement detection strategies that identify AI specific threats including prompt injection, model extraction, data poisoning, adversarial examples, and unauthorized access to training datasets or model weights across our distributed infrastructure. Build automated response playbooks and orchestration workflows that contain threats without human intervention, creating self healing security systems that reduce mean time to response from hours to minutes while automatically remediating compromised inference endpoints. Lead security incident response coordination across all teams (Cloud, AppSec, Enterprise, AI Security) when AI infrastructure or models are compromised, conducting forensic investigations on training pipeline attacks and model manipulation attempts while drafting clear incident communications for engineering and executive leadership. Hunt proactively for sophisticated threats across GPU clusters and training infrastructure by analyzing model outputs for signs of compromise, reproducing AI specific vulnerabilities from security research, and identifying visibility gaps in distributed training environments before adversaries exploit them. Build detection as code frameworks with version control and automated deployment, onboard telemetry from AI training infrastructure and inference endpoints, and create dashboards that track model security metrics, GPU utilization patterns, and access to sensitive research data. Collaborate cross functionally as the operational security partner for all teams - translating AI Security's threat research into production detections, monitoring Cloud Infrastructure's GPU clusters for threats, detecting customer impacting incidents for Software Security Engineering, and enabling responsible AI development through security guardrails. Maintain 24/7 on call rotation for critical AI security incidents, responding to real time threats targeting our platform while continuously improving detection coverage and automation capabilities as our AI systems evolve. What you need 3-5+ years in security operations, detection engineering, or incident response with a proven track record of identifying and stopping sophisticated attacks in production environments, plus 3+ years specifically securing AI/ML infrastructure, high performance computing environments, or other distributed systems at scale. Strong programming skills in Python, KQL, SPL, or similar languages that allow you to build custom detection logic, automate response workflows, and create tools that operationalize security at scale across cloud native and distributed computing environments. Experience with SIEM platforms, detection technologies, and forensic investigation techniques with demonstrated ability to build detection for novel attack techniques that don't have established patterns yet and to conduct forensics in complex distributed environments. Self directed execution mindset with a track record of securing high value intellectual property, automating incident response in complex environments, and identifying critical security gaps through proactive threat hunting before they become incidents. Deep alignment with WRITER's values - you naturally Connect across security, infrastructure, and AI research teams to build comprehensive defenses, you Challenge assumptions about what's possible in AI security engineering, and you Own the protection of our AI platform with unwavering accountability and a commitment to staying ahead of evolving threats. Benefits & perks (UK full time employees) Generous PTO, plus company holidays. Comprehensive medical and dental insurance. Paid parental leave for all parents (12 weeks). Fertility and family planning support. Early detection cancer testing through Galleri. Competitive pension scheme and company contribution. Annual work life stipends for: Wellness stipend for gym, massage/chiropractor, personal training, etc. Learning and development stipend. Company wide off sites and team off sites. Competitive compensation and company stock options.

What We're Looking For We are seeking a DevOps Engineer to build and own the infrastructure that underpins our AI driven materials discovery platform. You'll work directly with world renowned ML researchers and software engineers to accelerate real scientific breakthroughs by making model training, experimentation, and deployment fast, reliable, and reproducible. This is a foundational hire. You'll set the patterns others build on. You will be joining a small, highly ambitious team of world renowned engineers, AI researchers, and materials scientists. We move fast and value people who are energised by that. What You'll Do Design, provision, and manage cloud infrastructure (AWS/GCP) using infrastructure as code; Terraform, Pulumi, or equivalent. Own GPU compute environments for model training and inference, including cluster configuration, job scheduling, and cost optimisation. Build and maintain CI/CD pipelines that support rapid model iteration, automated testing, and safe deployments. Support ML workflow orchestration; experiment tracking, training run management, and data pipeline reliability. Ensure reproducibility across research and production environments through containerisation and rigorous environment management. Define monitoring, alerting, and incident response processes so the team can move fast without things silently breaking. Implement security best practices: secrets management, IAM, network segmentation, vulnerability scanning. Build internal tooling and documentation that lets researchers self serve infrastructure without waiting on you. Skills & Qualifications 4+ years in a DevOps, Platform Engineering, or SRE role. Strong proficiency with at least one major cloud provider and its core services (compute, storage, networking, IAM). Hands on experience with infrastructure as code and container orchestration (Kubernetes or equivalent). Solid CI/CD pipeline experience, GitHub Actions, GitLab CI, or similar. Proficient in Python and Bash; comfortable reading and writing code across a polyglot stack. Deep Linux systems knowledge and strong networking fundamentals. A bias for building things properly the first time, even under early stage constraints. Nice to Have Experience with GPU cluster management and ML training workloads (NVIDIA, CUDA, distributed training). Familiarity with MLOps tooling: Experiment tracking (MLflow, Weights & Biases). Workflow orchestration (Airflow, Prefect, Argo). Data versioning (DVC). Background in scientific computing or HPC environments. Prior experience at a deep tech or computational science company. Why Join Us Work directly on infrastructure that enables AI to make real scientific discoveries. Shape how we build from day one, no legacy systems, no inherited mess. Collaborate with world class researchers across materials science and machine learning. Diffractive is building the AI Material Scientist that autonomously learns from real world experimentation to push the boundaries of scientific discovery. We're early, moving fast, and working on problems that genuinely matter. We are a London based company with a flexible approach to how and where you work. We offer competitive salary, generous equity, and benefits. You'll have a real stake in what you build and in the company's overall success. Equal Opportunity Diffractive is an equal opportunities employer. We are committed to creating an inclusive environment for all employees and welcome applications from people of all backgrounds, experiences, and identities. If you require any adjustments or accommodations at any point during the interview process please let us know - we will be happy to help.

Remitly is a leading digital financial services provider for immigrants and their families in more than 170 countries. The Treasury team manages global funding operations, optimising liquidity, managing funding corridors, and ensuring capital availability where needed. About the Role The role reports to the Head of Treasury Analytics & Data Science (London) and is based in London (hybrid). We are seeking a builder who can move fast from idea to production-owning agentic systems end to end to increase the operational capacity of the Treasury team. The position sits at the intersection of large language models, Treasury operations, and data infrastructure. Key Responsibilities Build Treasury Agent Automation for cash flow monitoring agents: intraday cash position tracking, reconciliation, variance flagging, and automated daily reports across accounts and currencies. Build funding execution agents: instruction routing, approval workflows, payment scheduling, cut off monitoring, and settlement tracking encoded with business rules and risk parameters. Build narrative agents: auto generated end of day and intraday funding commentary, exception summaries, and context aware alerts delivered to the right stakeholders. Run discovery to deployment pipeline: work with Funding operators to identify manual tasks, then ship agents that eliminate them-reporting packs, utilisation dashboards, liquidity summaries. Embed within the Funding team at the outset, running existing workflows end to end alongside operators to develop a firsthand understanding of day to day processes, limits, and pain points before a single agent is scoped or built. Build reusable, modular agent components and tool libraries that scale as Remitly expands into new markets and corridors. Contribute to Treasury wide agent governance standards, evaluation frameworks, and shared infrastructure. Research and recommend emerging LLM capabilities and agentic tooling to keep the Funding function ahead of the curve. Partner closely with the Treasury Engineering team to develop the data accuracy, completeness, and timeliness required by Treasury Agents. Qualifications Advanced proficiency with coding agents across complex development scenarios. Experience architecting multi service systems, including complex persistence, integrations, and scaling. Understanding of distributed systems trade offs and cloud based infrastructure design. AI tool fluency: orchestrators, automated workflows, comprehensive evaluation pipelines. Advanced systems architecture expertise across the full technology stack. Proficiency in customer discovery techniques and ability to connect technical work to business outcomes. Competence with design thinking, analytics tools, and funnel analysis. Experience guiding others on AI first development approaches. Expertise in experiment modelling, funnel analysis, and data informed decision making. Track record of mentoring others and elevating team capabilities. Nice to Have Treasury, fintech, or payments domain knowledge (cash positioning, FX, liquidity). LLM observability tools: LangSmith, Arize, Weights & Biases. AWS (S3, Glue, Redshift, Lambda); TMS/financial data providers (Bloomberg, Kyriba). Startup, founder, or cross functional multi product experience. Benefits Paid vacation days. Health insurance. Commuter benefit. Employee Stock Purchase Plan (ESPP). Mental health & family forming benefits. Continuing education and corridor travel benefits. Remitly is an equal opportunity employer and complies with all applicable laws and regulations. We celebrate diversity and are committed to creating an inclusive environment.

Data Scientist The Commodities tribe at Kpler runs production ML models that predict what cargo a vessel is carrying (Product Estimation) and where in-transit vessels are headed (Destination Forecast), and where they are expected to arrive (ETA) - across LNG, DRY, LPG, and LIQUIDS. These predictions feed directly into Kpler's cargo intelligence platform, consumed by market analysts, trading desks, and external customers worldwide. You will own the science behind these models: designing and evaluating features from maritime AIS data, H3 geospatial routing distributions, transit statistics, and commodity-specific signals; running structured experiments on an ML Flow-based platform; and pushing the accuracy, coverage, and reliability of predictions forward. You are not handed a Jupyter notebook and a dataset. You work in a production system with real-time inference running every 1-3 hours across 4 commodity types, and your model changes need to be validated against a running parallel baseline before they go live. The new platform is being built specifically to make the experiment loop fast enough that this level of rigour does not slow you down. Key Responsibilities Own the feature engineering roadmap for ETA & Destination Forecast across all 4 commodity types - propose and implement new features as dbt models using Airflow to orchestrate the data pipelines, and validate their impact through structured experiments. Design and run experiments using kpler-ml framework, logging all runs from train to evaluation to MLflow and producing structured comparison reports against the production baseline before any promotion. Work directly with Commodities Market Analysts and product stakeholders to understand where prediction quality matters most commercially - and use that to prioritise the experiment backlog. Contribute to the drift monitoring setup - validate PSI/KS thresholds using MLFlow against historical inference batches; define what constitutes a meaningful drift signal for PE and DF specifically. Document experiment decisions in MLflow and Confluence documents - the experiment history is a first class artifact, not an afterthought. Experience & Background 2+ years applying ML to real-world production problems - not research or hackathon work, but models running in production with real consequences for errors. Experience with geospatial or sequential data - vessel trajectories, routing patterns, H3/S2 grid systems, or equivalent spatial representations. Python proficiency at a level sufficient to implement new features, write dbt models, and script experiments - not just use notebooks. Familiarity with MLflow or equivalent experiment tracking (Weights & Biases, Neptune, etc.). Desirable: Domain knowledge of maritime shipping, commodity trading, or cargo intelligence - understanding what a port call sequence or a vessel's draught profile means physically, not just statistically. Desirable: Familiarity with Redshift or columnar warehouses for large-scale feature queries and dbt (authoring or reading SQL models). Kpler is committed to providing a fair, inclusive and diverse work-environment. We believe that different perspectives lead to better ideas, and better ideas allow us to better understand the needs and interests of our diverse, global community. We welcome people of different backgrounds, experiences, abilities and perspectives and are an equal opportunity employer. By applying, I confirm that I have read and accept the Staff Privacy Notice.

About WRITER WRITER is where the world's leading enterprises orchestrate AI powered work. Our vision is to expand human capacity through superintelligence. And we've proven it's possible - through powerful, trustworthy AI that unites IT and business teams together to unlock enterprise wide transformation. With WRITER's end to end platform, hundreds of companies like Mars, Marriott, Uber, and Vanguard are building and deploying AI agents that are grounded in their company's data and fueled by WRITER's enterprise grade LLMs. Valued at $1.9B and backed by industry leading investors including Premji Invest, Radical Ventures, and ICONIQ Growth, WRITER is rapidly cementing its position as the leader in enterprise generative AI. Founded in 2020 with office hubs in San Francisco, New York City, Austin, Chicago, and London, our team thinks big and moves fast, and we're looking for smart, hardworking builders and scalers to join us on our journey to create a better future of work with AI. About the role Join WRITER's security team as a staff detection and response engineer and help protect the AI infrastructure that's transforming how the world works. You'll build sophisticated detection systems that identify attacks targeting our AI platform, training data, and model deployments while creating automated response capabilities that scale with our explosive growth. This isn't just traditional security work - you're defending cutting edge AI/AGI systems against adversaries who are evolving their tactics as fast as AI itself advances. This role combines hands on security engineering with strategic thinking to stay ahead of novel threats that don't exist in textbooks yet. You'll be the operational arm of our security function, translating threat intelligence into real time detections, coordinating incident response across multiple teams, and hunting for sophisticated attacks across GPU clusters and distributed training environments. If you're excited by the challenge of securing systems that are fundamentally different from anything you've protected before, this is your opportunity to define what AI security engineering looks like at scale. You'll work closely with our AI Security research team, Cloud Infrastructure, Software Security Engineering, and AI researchers to build a defense in depth strategy that protects one of the most valuable AI platforms in the industry. The threats are real, the stakes are high, and the problems are intellectually fascinating. This role can be based inSan reporting to our head of security operations. What you'll do Design and implement detection strategies that identify AI specific threats including prompt injection, model extraction, data poisoning, adversarial examples, and unauthorized access to training datasets or model weights across our distributed infrastructure Build automated response playbooks and orchestration workflows that contain threats without human intervention, creating self healing security systems that reduce mean time to response from hours to minutes while automatically remediating compromised inference endpoints Lead security incident response coordination across all teams (Cloud, AppSec, Enterprise, AI Security) when AI infrastructure or models are compromised, conducting forensic investigations on training pipeline attacks and model manipulation attempts while drafting clear incident communications for engineering and executive leadership Hunt proactively for sophisticated threats across GPU clusters and training infrastructure by analyzing model outputs for signs of compromise, reproducing AI specific vulnerabilities from security research, and identifying visibility gaps in distributed training environments before adversaries exploit them Build detection as code frameworks with version control and automated deployment, onboard telemetry from AI training infrastructure and inference endpoints, and create dashboards that track model security metrics, GPU utilization patterns, and access to sensitive research data Collaborate cross functionally as the operational security partner for all teams - translating AI Security's threat research into production detections, monitoring Cloud Infrastructure's GPU clusters for threats, detecting customer impacting incidents for Software Security Engineering, and enabling responsible AI development through security guardrails Maintain 24/7 on call rotation for critical AI security incidents, responding to real time threats targeting our platform while continuously improving detection coverage and automation capabilities as our AI systems evolve What you need 3 5+ years in security operations, detection engineering, or incident response with a proven track record of identifying and stopping sophisticated attacks in production environments, plus 3+ years specifically securing AI/ML infrastructure, high performance computing environments, or other distributed systems at scale Strong programming skills in Python, KQL, SPL, or similar languages that allow you to build custom detection logic, automate response workflows, and create tools that operationalize security at scale across cloud native and distributed computing environments Experience with SIEM platforms, detection technologies, and forensic investigation techniques with demonstrated ability to build detection for novel attack techniques that don't have established patterns yet and to conduct forensics in complex distributed environments Self directed execution mindset with a track record of securing high value intellectual property, automating incident response in complex environments, and identifying critical security gaps through proactive threat hunting before they become incidents Deep alignment with WRITER's values - you naturally connect across security, infrastructure, and AI research teams to build comprehensive defenses, you challenge assumptions about what's possible in AI security engineering, and you own the protection of our AI platform with unwavering accountability and a commitment to staying ahead of evolving threats Benefits & perks (UK full time employees) Generous PTO, plus company holidays Comprehensive medical and dental insurance Paid parental leave for all parents (16 weeks) Fertility and family planning support Early detection cancer testing through Galleri Competitive pension scheme and company contribution Annual work life stipends for: Wellness stipend for gym, massage/chiropractor, personal training, etc. Learning and development stipend Company wide off sites and team off sites Competitive compensation and company stock options

Design, train, and deploy production-grade ML models. About the Role As a Senior Machine Learning Engineer, you will lead the design, development, and deployment of production-grade machine learning systems for global enterprise clients. You'll work at the intersection of research and engineering, translating cutting edge papers into scalable, reliable solutions. This is a high impact role where you'll mentor junior engineers, shape our ML architecture, and collaborate directly with clients to solve their most complex challenges. Design and implement end-to-end ML pipelines from experimentation to production Lead model development across NLP, computer vision, and time series forecasting Architect scalable model serving infrastructure on cloud platforms (AWS/GCP/Azure) Conduct rigorous model evaluation, A/B testing, and performance monitoring Mentor junior ML engineers and conduct technical code reviews Collaborate with data engineers to optimise feature stores and data pipelines Stay current with latest ML research and evaluate applicability to client projects Requirements 5+ years of experience in machine learning engineering or applied ML research Strong proficiency in Python, PyTorch or TensorFlow, and Scikit-learn Experience deploying ML models at scale using Docker, Kubernetes, or serverless Deep understanding of ML fundamentals: optimisation, regularisation, evaluation metrics Experience with cloud ML services (SageMaker, Vertex AI, or Azure ML) Strong software engineering practices: version control, testing, CI/CD MSc or PhD in Computer Science, Machine Learning, or related field Nice to Have Published research in top ML venues (NeurIPS, ICML, ACL, CVPR). Experience with MLOps tools: MLflow, Weights & Biases, DVC. Familiarity with distributed training frameworks (DeepSpeed, Horovod). Experience with LLM fine-tuning and prompt engineering. Contributions to open-source ML projects. Competitive salary with annual performance bonus. £2,000 annual conference and learning budget. Private healthcare and dental cover. Hybrid working - flexible office/remote split. 30 days annual leave plus bank holidays. Stock option scheme and pension matching. Latest hardware (M-series MacBook Pro, GPU workstation access). About the Team Our AI Research team is a tight knit group of 8 researchers and engineers passionate about pushing the boundaries of applied AI. We publish at top venues, contribute to open-source, and work on problems spanning healthcare diagnostics, financial risk modelling, and autonomous systems. You'll have the freedom to explore new ideas while delivering real value to clients.

Senior Machine Learning Infrastructure Engineer London, United Kingdom About us PhysicsX is a deep-tech company with roots in numerical physics and Formula One, dedicated to accelerating hardware innovation at the speed of software. We are building an AI-driven simulation software stack for engineering and manufacturing across advanced industries. By enabling high-fidelity, multi-physics simulation through AI inference across the entire engineering lifecycle, PhysicsX unlocks new levels of optimization and automation in design, manufacturing, and operations - empowering engineers to push the boundaries of possibility. Our customers include leading innovators in Aerospace & Defense, Materials, Energy, Semiconductors, and Automotive. Note:We are currently recruiting for multiple positions, however please only apply for the role that best aligns with your skillset and career goals. The Role The Senior ML Infrastructure Engineer will extend and operate the infrastructure that powers our research model training, fine-tuning, and serving pipelines. You will be embedded within our Research function, partnering directly with ML engineers and research scientists to ensure they can train Large Physics Models efficiently and reliably at scale. Team Context In this role, you will be vertically embedded in Research, working daily with: Research Scientists who determine the model architectures and methods ML Engineers who implement and develop the models Simulation Data Engineers who are accountable for upstream data pipelines You will have end-to-end responsibilities over the research infrastructure, with the autonomy to make architectural decisions and the responsibility to keep data flowing reliably. Horizontally, you will be part of an infrastructure engineering group responsible for infrastructure across the company. What you will do Training Infrastructure Design and operate distributed training infrastructure for neural operator architectures (Transolver, Point Cloud Transformer, etc.) on our large NVIDIA DGX B200 platform. Optimize training pipelines for throughput, fault tolerance, and cost efficiency, including checkpointing strategies, gradient accumulation, and multi-node synchronization. Build and maintain experiment tracking and observability systems that give researchers clear visibility into training runs, hyperparameter sweeps, and model performance. Data I/O and Performance Solve data loading bottlenecks for large-scale mesh datasets. Optimize data pipelines for efficient I/O from cloud storage, including prefetching, caching, and format optimization. Work with heterogeneous data sources of varying formats and resolutions. Model Serving and Deployment Build serving infrastructure for pre-trained LPMs, supporting both zero shot inference and uncertainty quantification (Monte Carlo Dropout). Design and implement model packaging pipelines for customer deployment. Models must run reliably in customer environments with fine tuning capabilities. Ensure reproducibility: any model checkpoint should be deployable with consistent behaviour. Platform and Tooling Improve developer experience for the Research team with fast iteration cycles, reliable CI/CD, clear debugging tools. Collaborate with the broader Infrastructure team on shared patterns and standards. What you bring to the table Ability to scope and effectively deliver projects, prioritising activity as needed. Problem solving skills and the ability to analyse issues, identify causes, and recommend solutions quickly. Excellent collaboration and communication skills, especially in a research setting. You can translate "the model isn't converging" into infrastructure hypotheses and solutions, and can bridge technical abstractions with implementations. 5+ years of experience building and operating ML infrastructure at scale: Deep expertise in distributed training: you've debugged NCCL hangs, optimized collective communication, and know when to use FSDP vs. DDP vs. pipeline parallelism Strong systems fundamentals: Linux, networking (including domain specific NVLink and InfiniBand), storage I/O, profiling and performance optimization Production experience with Kubernetes and SLURM for job orchestration on GPU clusters Proficiency in Python and ML frameworks (PyTorch strongly preferred) Experience with cloud GPU infrastructure; ideally CoreWeave or similar GPU/HPC-focused clouds Ideally Experience with geometric deep learning or neural operators, architectures that operate on meshes, point clouds, or graphs Background in HPC for simulation engineering, familiarity with how CFD/FEA workflows generate and consume data Experience building model serving infrastructure with latency and throughput requirements Familiarity with experiment tracking tools (Weights & Biases, MLflow) and observability stacks (Prometheus, Grafana) What we offer Equity options - share in our success and growth. 10% employer pension contribution - invest in your future. Free office lunches - great food to fuel your workdays. Flexible working - balance your work and life in a way that works for you. Hybrid setup - enjoy our new Shoreditch office while keeping remote flexibility. Enhanced parental leave - support for life's biggest milestones. Private healthcare - comprehensive coverage Personal development - access learning and training to help you grow. Work from anywhere - extend your remote setup to enjoy the sun or reconnect with loved ones. We value diversity and are committed to equal employment opportunity regardless of sex, race, religion, ethnicity, nationality, disability, age, sexual orientation or gender identity. We strongly encourage individuals from groups traditionally underrepresented in tech to apply. To help make a change, we sponsor bright women from disadvantaged backgrounds through their university degrees in science and mathematics. We collect diversity and inclusion data solely for the purpose of monitoring the effectiveness of our equal opportunities policies and ensuring compliance with UK employment and equality legislation. This information is confidential, used only in aggregate form, and will not influence the outcome of your application.

Full Stack Software Engineer, £50-85k, 3 days per week in London - Ground-breaking Fitness tech! About Us MAGIC AI is an elegant in home health coach that utilises computer vision, connected weights, and 3D cameras to provide personalised training sessions led by world renowned athletes. We have gained recognition and exposure, being stocked in Selfridges, featured on Good Morning Britain, and listed as one of Fast Company's World's Most Innovative Companies of 2024. With just a small team, our company achieved a substantial revenue rate during its first financial year. We're looking for a Full Stack Software Engineer to join our small, high performing engineering team. You'll be a core part of building an elegant in home health coach that uses computer vision, connected weights, and 3D cameras to provide personalised training. This is a unique opportunity to directly impact thousands of users daily by implementing impactful and compelling features. What You'll Be Involved In Work on exiting new features for our products. Mirror App, Mobile Apps and web applications. Collaborate closely with our product team to ensure product requirements are understood and executed flawlessly. Work with QA to resolve bugs and ensure smooth releases of new features. Communicate and coordinate with Customer Support and Marketing to ensure a smooth feature rollout. You Should Have Good experience with AI assisted software development Good experience with a range of full stack technologies Proficiency in using JIRA or other project management tools. Outstanding communication abilities, particularly in explaining complex technical concepts clearly to non technical individuals and writing detailed project documentation. A meticulous and thorough approach with exceptional attention to detail. Experience collaborating effectively in Agile Scrum teams. Behaviourally Self motivated and capable of working independently, taking full ownership of your tasks. Able to work effectively and cooperatively in a fast paced team setting. You don't have to be a regular gym goer, but you absolutely must be passionate about developing technology that will revolutionise how people exercise! Nice to Haves Experience with AWS technologies including SAM, CloudFormation, Lambda, API Gateway, DynamoDB, and S3. Experience in mobile development. Experience with Flutter Product minded with a strong understanding of how to build compelling products. A passion for fitness, ideally with experience building fitness products and working out yourself. Competitive salary Share Options in the company Unlimited Holiday (self directed time off) Flexible Home/Hybrid Working from our London HQ (At least 2 days WFH per week) Mental Health Wellbeing support Hardware budget for brand new Macbook or other Professional learning & development budget All. The. Fun. Regular awesome socials An impact from day one. Our business is scaling by the day. You'll work on ambitious projects, and your contribution will significantly impact the success of MAGIC AI now and in the future