PhysicsX Ltd

Staff Machine Learning Software Engineer, Research 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 across different levels, however please only apply for the role that best aligns with your skillset and career goals. What you will do Shape Research group strategy and culture in a significant way, especially in domains of expertise. Be opinionated and formulate strategy on engineering topics relevant to our Research priorities, especially on: scaled engineering, securing compute, infrastructure stack. Define necessary profiles to execute this strategy. Promote effective working patterns and proactively flag issues with team dynamics to foster a productive environment. Nurture younger colleagues to grow their skillset and guide their professional development. Own Research work-streams at a high-level to deliver outcomes. Align priorities with problem stakeholders, internal and external. Set the technical direction for the stream and apply judgement and taste to drive progress. Plan roadmaps with clear milestones for key decisions and outcomes. Organise and guide the more junior members of the team to effectively execute and deliver against this roadmap. Communicate purpose and key outcomes to raise awareness across the company and create opportunities for use and deployment. The below activities in particular. Work closely with our research scientists and simulation engineers to build and deliver models that address real-world physics and engineering problems. Design, build and optimise machine learning models with a focus on scalability and efficiency in our application domain. Transform prototype model implementations to robust and optimised implementations. Implement distributed training architectures (e.g., data parallelism, parameter server, etc.) for multi-node/multi-GPU training and explore federated learning capacity using cloud (e.g., AWS, Azure, GCP) and on-premise services. Work with research scientists to design, build and scale foundation models for science and engineering; helping to scale and optimise model training to large data and multi-GPU cloud compute. Identify the best libraries, frameworks and tools for our modelling efforts to set us up for success. Discuss the results and implications of your work with colleagues and customers, especially how these results can address real-world problems. Work at the intersection of data science and software engineering to translate the results of our Research into re usable libraries, tooling and products. Foster a nurturing environment for colleagues with less experience in ML / Engineering for them to grow and you to mentor. What you bring to the table Enthusiasm about developing machine learning solutions, especially deep learning and/or probabilistic methods, and associated supporting software solutions for science and engineering. Ability to work autonomously and scope and effectively deliver projects across a variety of domains. Strong problem-solving skills and the ability to analyse issues, identify causes, and recommend solutions quickly. Excellent collaboration and communication skills - with teams and customers alike. MSc or PhD in computer science, machine learning, applied statistics, mathematics, physics, engineering, software engineering, or a related field, with a record of experience in any of the following: scientific computing; high-performance computing (CPU / GPU clusters); parallelised / distributed training for large / foundation models. 4 years of experience in a professional industry setting, where you have been instrumental in most of the below: scaling and optimising ML models, training and serving foundation models at scale (federated learning a bonus); employing distributed computing frameworks (e.g., Spark, Dask) and high-performance computing frameworks (MPI, OpenMP, CUDA, Triton); employing cloud computing (on hyper scaler platforms, e.g., AWS, Azure, GCP); building machine learning models and pipelines in Python, using common libraries and frameworks (e.g., NumPy, SciPy, Pandas, PyTorch, JAX), especially including deep learning applications; building or using C/C++ for computer vision, geometry processing, or scientific computing; following and promoting software engineering concepts and best practices (e.g., versioning, testing, CI/CD, API design, MLOps); container ising and orchestrating compute tasks (Docker, Kubernetes, Slurm); writing pipelines and experiment environments, including running experiments in pipelines in a systematic way. What we offer Build what actually matters Help shape an AI native engineering company at a formative stage, tackling problems that genuinely matter for industry and society. This is work with real world impact - and something you can be proud to stand behind. Learn alongside exceptional people Work with a high caliber, collaborative team of engineers, scientists, and operators who care deeply about doing great work, and about helping each other get better. We come from diverse backgrounds, but we share a commitment to operating at the highest level and addressing some of the most complex challenges out there. If you're ambitious, thoughtful, and driven by impact, you'll feel at home. Influence over hierarchy We operate with a flat structure: good ideas win- wherever they come from. Questioning assumptions and challenging the status quo isn't just welcomed, it's expected. Building meaningful technology is a marathon, not a sprint. We believe in balancing focused, ambitious work with a life beyond it. Our hybrid model blends time together in our Shoreditch office with work from home days, giving you the flexibility to work sustainably while staying connected in person. And it doesn't stop there Equity options - share meaningfully in the company you're helping to build. 10% employer pension contribution - because investing in future matters. Free office lunches - to keep you energised and focused. Enhanced parental leave - 3 months full pay paternity and 6 months full pay maternity leave, to provide extra flexibility during the moments that matter most. YellowNest nursery scheme - to help working parents manage childcare costs. 25 days of Annual Leave (+ Public Holidays) - because taking time to rest matters. Private medical insurance - 100% employee cover, giving you complete peace of mind. Wellhub Subscription - gain access to thousands of gyms, classes and wellness apps, supporting both physical and mental wellbeing. Eye tests - because good work depends on good health. Personal development - dedicated support for learning, development, and leveling up over time. Employee Assistance Programme (EAP) - confidential wellbeing support, available whenever you need it. Bike2Work scheme and Season ticket loan - to make getting to work easier and greener. Octopus EV salary sacrifice - for a simpler, more sustainable way to drive electric. 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.

A leading deep-tech company is seeking a Senior Software Engineer to join their Core Services team in London. The ideal candidate will be responsible for building foundational systems for an AI-driven simulation software platform. Responsibilities include designing authentication systems, implementing access control models, and maintaining telemetry infrastructure to ensure high observability. Candidates should have strong foundations in software engineering, expertise in Golang and Python, and a passion for engineering excellence.

Senior Software Engineer - Core Services London 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. The Role PhysicsX is building a platform that enables Data Scientists and Simulation Engineers to build, train, and deploy Deep Physics Models. Our platform serves multiple tenants across highly regulated industries, making robust identity, access control, and operational visibility foundational to everything we do. We're looking for a Senior Software Engineer to join our Core Services team, the team building the foundational primitives that gives our customers superpowers they've never had before. For decades, engineers have been trapped using siloed, rigid tools from the 90s. We're changing that by enabling secure, permissioned collaboration at the frontier of AI. You'll design and build the enterprise grade systems that make this possible. From fine-grained permissions to instrumentation, you'll create the primitives that every product across PhysicsX adopts, ensuring every user, service, and agentic workflow is securely identified, correctly scoped, and fully observable. You'll own the infrastructure that keeps our platform trustworthy, transparent, and ready for the future of manufacturing and engineering. What You Will Do Design and implement authentication and authorisation systems, including identity provider integrations, token management, session handling, and SSO flows. Architect and build fine granular role based and attribute based access control (RBAC/ABAC) models that scale across multi tenant environments. Own the platform's permissions layer end to end: from policy definition and enforcement through auditing and compliance reporting. Build and maintain telemetry infrastructure distributed tracing, structured logging, metrics collection, and alerting, to provide deep observability across services and environments. Define and enforce security standards across APIs and services, including schema governance, data segregation, and least privilege access patterns. Design and implement identity and access patterns for AI agents, including MCP authentication, agent impersonation flows, and system account models that allow agents to act securely on behalf of users in a regulated, multi tenant environment. Contribute to the design of multi service architectures, ensuring authentication and authorisation concerns are cleanly integrated and consistently enforced across both human and agentic workflows. Drive best practices in CI/CD, automated testing, observability, and infrastructure as code. Build and maintain deployment pipelines, including zero downtime and multi service deployments. Author and review Technical Decision Records. Participate in technology reviews to evaluate and adopt new tools and approaches. Mentor junior and mid level engineers, facilitate technical discussions, and build consensus around architectural decisions. What you bring to the table A passion for the craft, you're driven by engineering excellence and committed to fostering that culture across the team. Strong software engineering foundations, solid grasp of algorithms, data structures, and system design. You write clean, maintainable, testable code and have strong command of Golang and Python. Authentication and identity expertise, hands on experience building or integrating identity and access management systems (e.g., Keycloak, Auth0, Okta). Deep understanding of OAuth 2.0, OIDC, SAML, and token based authentication flows. Ability to effectively leverage cloud provider IAM systems (e.g., AWS IAM, GCP IAM, Azure AD). Authorisation and permissions design, proven experience implementing RBAC, ABAC, or policy as code frameworks (e.g., OPA/Rego, Cedar) in production multi tenant systems. Telemetry and observability, experience designing and operating metrics, tracing, and logging pipelines (e.g., OpenTelemetry, Prometheus, Grafana, Jaeger). You understand what it takes to make distributed systems genuinely observable. Kubernetes and GitOps, strong working knowledge of Kubernetes and ArgoCD, including deploying, managing, and troubleshooting services in production clusters. API and service design maturity, experience designing multi service systems with attention to schema governance, forward compatibility, and secure data access patterns. Proven ability to develop schema drift mitigation strategies with minimal impact to dependent clients (e.g., forward compatible schemas, ACLs, ambassador sidecars). Agent governance awareness, understanding of how identity and permissions extend to agentic systems, including MCP auth, impersonation, system accounts, and scoped token delegation. Security awareness, familiarity with threat modelling, secure coding practices, and participating in security testing and compliance workflows. CI/CD and deployment expertise, hands on experience building and optimising CI/CD pipelines, including multi service and zero downtime deployments across numerous customer environments. Communication and collaboration, excellent communication skills to work across teams, understand requirements from research scientists and product stakeholders, and translate them into technical specifications. AI native mindset, you follow the frontier of agentic systems and AI tooling. You naturally think about how infrastructure needs to evolve when agents, not just humans, are the consumers of your APIs and services. You actively use AI coding tools (e.g., Copilot, Cursor, Claude Code) in your daily workflow and see them as a force multiplier, not a novelty. Incremental mindset, you work in small steps toward larger goals, driving change through continuous improvement rather than massive redesigns. You can zoom in on details and zoom out to see the big picture. Ideally Polyglot programming, deep expertise in Python and Golang, with exposure to other languages such as Rust or C++. Advanced Kubernetes, ability to leverage resources that extend the Kubernetes API (e.g., CRDs, Operators) and infrastructure configuration tools (Crossplane, Helm charts). Infrastructure flexibility, understanding of what it takes to build software that runs in cloud, on premises, and air gapped environments. Advanced testing, experience with fuzzing, deterministic simulation testing, or fault injection in production systems. 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.

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 Simulation Data Engineer will extend and operate the infrastructure that powers our research Data Factory. You will be responsible for the end-to-end pipeline: from geometry preparation and simulation orchestration through validation, post-processing, and delivery to downstream ML training systems, using PhysicsX platform orchestration services where synergies exist. This role sits at the intersection of HPC engineering and data engineering. You will orchestrate long-running CFD simulations at scale, build robust data pipelines, and ensure that every simulation we produce meets rigorous quality standards. Team Context In this role, you will be vertically embedded in Research , working daily with: Research Scientists who define data requirements and quality standards ML Engineers who consume Data Factory outputs for model training ML Infrastructure Engineers who are accountable for downstream training infrastructure You will have end-to-end responsibilities over the Data Factory, 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 Simulation Orchestration Extend and operate the Data Factory infrastructure that orchestrates thousands of CFD simulations per day on cloud compute Design and operate job scheduling systems that maximize throughput while handling failures gracefully Build monitoring and alerting to detect simulation failures, convergence issues, and resource bottlenecks early Build high-performance data pipelines that move simulation outputs from solver results to ML-ready training data Implement geometry preprocessing workflows (mesh preparation, morphing, watertightness validation) Design and operate post-processing pipelines: surface decimation, field interpolation, format conversion Optimize I/O performance for large mesh datasets Data Quality and Validation Implement comprehensive validation checks at every pipeline stage: solver convergence, physical field bounds, post-processing fidelity Build systems that capture and quarantine bad data before they reach training pipelines Track and report data quality metrics across the entire Data Factory Work towards full provenance: training samples should be traceable back to their source geometry and simulation configuration Integration and Delivery Deliver validated datasets to downstream ML training infrastructure in formats optimized for efficient data loading Design data versioning and cataloging systems that support reproducible training runs Work closely with ML Infrastructure Engineers to ensure smooth handoff between data production and model training Support multi-dataset training workflows 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 in data engineering, HPC engineering, or simulation infrastructure. Strong experience with orchestration systems: SLURM, Kubernetes, Temporal Production data pipeline experience: you've built and operated pipelines that process large volumes of data reliably Proficiency in Python for pipeline development and automation Systems engineering fundamentals: Linux, networking, storage systems, performance debugging Experience with cloud infrastructure; ideally CoreWeave or similar GPU/HPC focused clouds Background in HPC for simulation engineering: experience with CFD, FEA, or similar computational workflows (StarCCM+, OpenFOAM, ANSYS, etc.) Experience with geometry processing: mesh manipulation, CAD formats, PyVista Familiarity with scientific data formats: HDF5, VTK, NetCDF, Zarr Data quality engineering experience: validation frameworks, anomaly detection, data observability Ideally Understanding of CFD fundamentals, enough to interpret solver outputs and validation metrics Experience with 3D geometry pipelines (mesh decimation, field interpolation) Familiarity with ML data loading patterns and how training systems consume data 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.

A cutting-edge technology company in Greater London is seeking a Senior Simulation Data Engineer to extend their Data Factory infrastructure. This position involves orchestrating large-scale CFD simulations and ensuring the quality and reliability of simulation data. Candidates should have over five years of experience in data engineering and HPC, with strong skills in Python and orchestration systems. The position offers a hybrid work model with benefits including equity options and private healthcare.

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.

A deep-tech company in London is seeking a Senior Machine Learning Infrastructure Engineer to enhance and manage the infrastructure for model training and deployment. You will collaborate with ML engineers and research scientists to ensure effective model training at scale. The ideal candidate should have at least 5 years of experience in ML infrastructure, strong problem-solving skills, and proficiency in distributed training technologies. This position offers equity options, a 10% pension contribution, and a hybrid work setup.

PhysicsX Ltd is seeking a Senior Software Security Developer for their Core Platform Services team in London. This role involves writing secure code components crucial for various teams across the company. The ideal candidate has over 8 years of experience and a strong focus on security feature design, authentication workflows, and extensive knowledge of secure coding practices, including OWASP Top 10. Additional perks include equity options, private medical insurance, and a hybrid working model.

Senior Software Security Developer - Core Platform Services London 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. The Role We are recruiting for a Senior Software Security Developer within our Core Platform Services Team. You will be responsible primarily for writing secure code components that many teams will use across the business. There is a strong emphasis on authentication for this role. What you will do Design and implement platform security features and guardrails. Act as an SME for security for the Core Services development team, including mentoring, performing threat modeling, and security code reviews. Help shape incident response procedures and vulnerability management workflows. Support the response team by validating and remediating product security vulnerabilities. Contribute to secure coding standards and provide training/mentorship to developers. What you bring to the table 8+ years in a developer role focused with strong focus on designing and building security features Extensive RBAC/ABAC knowledge and implementation experience Experience with user, agent, and machine authentication workflows. Hands on experience with secure coding, OWASP Top 10, and threat modeling. Strong developer skills and extensive experience shipping code to production. Experienced in CI/CD, IaC, Python and Go. Track record of balancing pragmatism and security rigor in a fast paced team. Thorough knowledge of authentication and authorization protocols (OAuth, OpenID Connect, SAML, LDAP, etc.). Strong communication skills, comfortable working across development teams and managing multiple initiatives. Nice to Have Skills Strong understanding of AI security fundamentals Participation in bug bounty programs Familiarity with the BSIMM framework Experience in cloud security including identity and access management and cloud native services. What we offer Build what actually matters Help shape an AI native engineering company at a formative stage, tackling problems that genuinely matter for industry and society. This is work with real world impact - and something you can be proud to stand behind. Learn alongside exceptional people Work with a high-caliber, collaborative team of engineers, scientists, and operators who care deeply about doing great work, and about helping each other get better. We come from diverse backgrounds, but we share a commitment to operating at the highest level and addressing some of the most complex challenges out there. If you're ambitious, thoughtful, and driven by impact, you'll feel at home. Influence over hierarchy We operate with a flat structure: good ideas win wherever they come from. Questioning assumptions and challenging the status quo isn't just welcomed, it's expected. Building meaningful technology is a marathon, not a sprint. We believe in balancing focused, ambitious work with a life beyond it. Our hybrid model blends time together in our Shoreditch office with work from home days, giving you the flexibility to work sustainably while staying connected in person. Benefits Equity options - share meaningfully in the company you're helping to build. 10% employer pension contribution. Free office lunches. Enhanced parental leave - 3 months full pay paternity and 6 months full pay maternity leave. YellowNest nursery scheme - to help working parents manage childcare costs. 25 days of annual leave (+ public holidays). Private medical insurance - 100% employee cover. Wellhub Subscription - gain access to thousands of gyms, classes and wellness apps. Eye tests. Personal development - dedicated support for learning, development, and leveling up over time. Employee Assistance Programme (EAP) - confidential wellbeing support. Bike2Work scheme and Season ticket loan - to make getting to work easier and greener. Octopus EV salary sacrifice - for a simpler, more sustainable way to drive electric. 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.

PhysicsX Ltd is seeking a Staff Machine Learning Software Engineer to join their team in London, UK. This role involves shaping the research strategy and culture, delivering key outcomes in collaboration with engineers and scientists, and working on high-impact machine learning projects. Candidates should possess a MSc or PhD and have at least 4 years of relevant experience. The company offers a hybrid work model with a range of benefits including equity options and private medical insurance.

PhysicsX Ltd is seeking a Machine Learning Software Engineer in London to build AI-driven simulation software for engineering. You will collaborate with scientists, design machine learning models, and implement distributed architectures, contributing to optimization in various industries including aerospace and automotive. Ideal candidates will hold a MSc or PhD in a relevant field and exhibit strong collaboration skills. The position offers a hybrid work model and a comprehensive benefits package.