14 jobs found

CoreWeave is The Essential Cloud for AI . Built for pioneers by pioneers, CoreWeave delivers a platform of technology, tools, and teams that enables innovators to build and scale AI with confidence. Trusted by leading AI labs, startups, and global enterprises, CoreWeave combines superior infrastructure performance with deep technical expertise to accelerate breakthroughs and turn compute into capability. Founded in 2017, CoreWeave became a publicly traded company (Nasdaq: CRWV) in March 2025. We're proud to be a Living Wage accredited Employer. What You'll Do: The Monolith AI Platform Engineering Team at CoreWeave is responsible for building and scaling the data and workflow backbone that powers the world's most advanced engineering simulation and AI workflows - our ambition is to become the super intelligent AI test lab for the engineering industry, helping customers ship science, faster. From high throughput data ingestion and feature pipelines to model training and real time inference, our platform delivers the performant, reliable, and trustworthy data foundation trusted by the world's largest engineering companies. The Senior DataOps Engineer II will own and drive all things data observability and operations across our client estate - building the practices, tooling, and culture that make Monolith's data flows debuggable, auditable, and safe to evolve. You'll sit at the intersection of platform engineering, data engineering, and reliability, implementing end to end lineage and DataOps practices while mentoring data producers and consumers on how to manage data as a first class product. You'll partner closely with Monolith's Product, Engineering and forward deployed teams, as well as with CoreWeave's infrastructure and AI platform groups, to turn fragmented, real world engineering data into well governed, observable, and operationally robust pipelines powering our SaaS platform and client specific deployments. About the Role: We're seeking an Senior DataOps Engineer II who can act as the hands on owner for Monolith's data observability and operational surface: from batch and streaming pipelines running on our platform, through to the lineage, quality, and runbooks that keep customer environments healthy. You'll define and roll out DataOps practices (CI/CD, infra as code, data SLOs, incident response) across the Monolith estate, implement end to end data lineage and observability, and serve as the go to mentor for engineering teams and client facing colleagues on best practice data management. In this role, you will: Own Monolith's Data Observability & Operations Surface Design and implement the end to end observability stack for data workloads (metrics, logs, traces, and data quality signals) across batch and streaming pipelines. Define and maintain operational SLOs/SLAs for critical data flows powering training, inference, and analytics, and ensure they are measurable and actionable. Build dashboards, alerts, and runbooks that allow engineers and on call responders to quickly detect, triage, and remediate data incidents. Standardise "golden paths" for how teams instrument pipelines, expose health signals, and respond to data related failures. Implement Data Lineage, Quality & Governance Deploy and maintain end to end data lineage for key domains - from client sources through transformations to features, models, and downstream analytics so teams can debug, audit, and reason about change. Define and roll out data quality checks (schema, freshness, completeness, distribution, drift) and ensure failures integrate cleanly into alerting and incident workflows. Partner with Security, Compliance, and customer facing teams to encode data governance requirements (e.g., retention, residency, access controls) into our pipelines and tooling. Help shape metadata models and catalog conventions so that producers and consumers can reliably discover, understand, and use shared datasets. Enable DataOps Practices Across Teams Establish CI/CD patterns for data pipelines and related infrastructure, including testing strategies, promotion workflows, and change management guardrails. Drive adoption of infra as code for data infrastructure (e.g., pipeline orchestration, storage, observability components), reducing manual drift across environments. Define and continuously improve DataOps processes - incident response, post incident review, change review, on call rotations - with a focus on learning rather than blame. Evaluate and integrate best of breed DataOps and observability tooling where it accelerates our teams, balancing build vs. buy pragmatically. Partner Across Monolith, CoreWeave & Clients Work with Monolith platform, data, agent, and reliability teams to expose observability and lineage as shared services and patterns other engineers can build on. Collaborate with CoreWeave infrastructure and AI platform teams to leverage underlying storage, compute, networking, and observability in service of robust data flows. Serve as a technical escalation point for forward deployed and customer facing engineers when data issues cross service boundaries or require deeper architectural insight. Mentor data producers (product teams, integrations, forward deployed engineers) and data consumers (data scientists, analysts, client engineers) on resilient schemas, contracts, and operational practices. Who You Are: Experience & Level Typically 5-6+ years of experience in DataOps, Data Engineering, DevOps/SRE for data platforms, or similar roles, including end to end ownership of production data pipelines and their operations. Proven track record of operating at Senior IC scope: leading cross team initiatives, introducing new practices/tooling, and improving reliability at the platform level. DataOps, Pipelines & Tooling Strong hands on experience designing, deploying, and operating data pipelines in production (batch and/or streaming), including failure modes, retries, and backfills. Practical experience with data orchestration and ETL/ELT tooling (e.g., Airflow, Dagster, dbt, Temporal, or similar) and comfort evaluating and integrating new tools where appropriate. Solid SQL and/or Spark skills and experience with at least one major analytical database or warehouse; familiarity with time series / telemetry data is a plus. Observability, Lineage & Data Quality Extensive experience implementing data observability - metrics, logging, tracing, dashboards, and alerting - for data centric workloads. Hands on work with data quality frameworks and/or observability platforms to monitor freshness, completeness, schema changes, and anomalies. Experience deploying and using data lineage or metadata/catalog solutions, and applying them to debugging, compliance, and change impact analysis. Platform, Infrastructure & Automation Comfortable working in containerised, cloud native environments (Kubernetes plus at least one major cloud provider); experience with GPU or compute intensive workloads is a bonus. Strong automation mindset: infra as code, CI/CD, and configuration management for data infrastructure and observability components. Proficient in Python for building tooling, pipeline glue, and platform integrations; additional languages are a plus. Collaboration, Mentorship & Communication Clear communicator who can explain complex data flows and failure modes to both deeply technical and non specialist audiences. Experience mentoring engineers and data practitioners on better data management, observability, and operational hygiene - through documentation, examples, reviews, and office hours. Comfortable working in a fast moving, high ambiguity environment where we balance rapid iteration with the safety and reliability demanded by enterprise engineering clients. Preferred: Experience in ML/AI platforms or MLOps environments where data pipelines power experimentation, training, and inference at scale. Background with test, simulation, or time series data (e.g., physical test benches, battery labs, automotive/aerospace R&D). Familiarity with feature stores, experiment tracking, or model registries and their interaction with upstream data pipelines. Prior work in multi tenant SaaS platforms, especially those with strong compliance, observability, and uptime requirements. Experience supporting or partnering closely with forward deployed / professional services teams in complex customer environments. Wondering if you're a good fit? We believe in investing in our people, and value candidates who bring diverse experiences - even if you don't tick every single box. Here are a few qualities we've found compatible with our team. If some of this sounds like you, we'd love to talk: Data obsessed operator - You care deeply about making data systems observable, predictable, and easy to reason about, not just "working most of the time." Systems thinker - You enjoy mapping complex data flows across services, understanding failure modes, and designing for graceful degradation and rapid recovery. Pragmatic - You know when to build the ideal abstraction and when to ship the smallest change that meaningfully reduces risk or toil. Collaborative mentor . click apply for full job details

CoreWeave is The Essential Cloud for AI . Built for pioneers by pioneers, CoreWeave delivers a platform of technology, tools, and teams that enables innovators to build and scale AI with confidence. Trusted by leading AI labs, startups, and global enterprises, CoreWeave combines superior infrastructure performance with deep technical expertise to accelerate breakthroughs and turn compute into capability. Founded in 2017, CoreWeave became a publicly traded company (Nasdaq: CRWV) in March 2025. Learn more at . We're proud to be a Living Wage accredited Employer. What You'll Do: The Data Science team is focused on developing an advanced reliability platform. This system covers various aspects of data processing and analysis, including data intake, deriving meaningful metrics, identifying unusual patterns, predicting potential issues, finding slow processes in distributed systems, and using automated analysis to determine causes. We collaborate closely with internal teams like Fleet, Infrastructure, and AI Platform to enhance system stability, optimize resource use, shorten resolution times, and maintain service availability and financial performance. About the role: As a Senior Data & MLOps Engineer, you will design and scale the infrastructure supporting the GPU Intelligence Platform. This involves building pipelines for handling data, features, model training, and delivering insights and predictions for system health and optimization. You will transition the system from initial prototypes to a production environment operating across the fleet, focusing on scalability, separating real time service from periodic processing, and dynamic resource management based on system load and data frequency. You will architect and deploy these scalable distributed services using orchestration technologies. Key responsibilities: Design and implement scalable data ingestion pipelines. Build feature processing and baseline computation systems. Productionize models for prediction and detection. Develop and operate low latency service and robust offline workflows. Architect horizontally scalable services with clear separation between components, leveraging orchestration for distribution. Implement monitoring and feedback loops for continuous model and signal improvement. Collaborate with Platform teams to integrate operational signals into monitoring and diagnostics. Implement a scalable solution for mitigation and structured analysis. Who You Are: 7+ years of experience in data engineering, distributed systems, MLOps, or infrastructure ML roles in production environments. Proven experience building high-throughput streaming or telemetry pipelines (e.g., Kafka, Pulsar, Kinesis, or equivalent). Strong experience designing time series feature pipelines and operating large scale observability systems. Experience building and maintaining feature stores and ensuring offline/online feature parity. Hands on experience deploying ML models to production, including versioning, monitoring, rollback, and drift detection. Experience designing scalable microservices deployed in Kubernetes based environments. Strong proficiency in Python and at least one systems language (Go, Rust, or C++). Experience working with distributed compute or training systems (e.g., NCCL, PyTorch Distributed, Spark, Ray, Slurm). Familiarity with GPU telemetry systems such as NVML or DCGM and hardware level monitoring concepts. Demonstrated experience scaling systems from Proof of Concept to production grade, fleet level deployments. Preferred: Experience working on GPU fleet management, hyperscale infrastructure, or AI training clusters. Experience building anomaly detection or failure prediction systems for hardware or distributed systems. Experience implementing distributed straggler detection or collective level performance analysis systems. Experience developing agentic or LLM powered reasoning systems for diagnostics or operational intelligence. Background in reliability engineering or SRE practices. Wondering if you're a good fit? We believe in investing in our people, and value candidates who can bring their own diversified experiences to our teams - even if you aren't a 100% skill or experience match. Here are a few qualities we've found compatible with our team. If some of this describes you, we'd love to talk. You love building systems that turn raw infrastructure telemetry into actionable intelligence. You're curious about distributed systems failure modes, GPU performance pathologies, and reliability engineering at scale. You're excited by the idea of moving from anomaly detection to prediction to autonomous root cause reasoning. You enjoy designing platforms that protect uptime, revenue, and customer trust through proactive systems thinking. Why CoreWeave? At CoreWeave, we work hard, have fun, and move fast! We're in an exciting stage of hyper growth that you will not want to miss out on. We're not afraid of a little chaos, and we're constantly learning. Our team cares deeply about how we build our product and how we work together, which is represented through our core values: Be Curious at Your Core Act Like an Owner Empower Employees Deliver Best in Class Client Experiences Achieve More Together We support and encourage an entrepreneurial outlook and independent thinking. We foster an environment that encourages collaboration and enables the development of innovative solutions to complex problems. As we get set for takeoff, the organization's growth opportunities are constantly expanding. You will be surrounded by some of the best talent in the industry, who will want to learn from you, too. Come join us! What We Offer In addition to a competitive salary, we offer a variety of benefits to support your needs, including: Family-level Medical Insurance Family-level Dental Insurance Generous Pension Contribution Life Assurance at 4x Salary Critical Illness Cover Employee Assistance Programme Tuition Reimbursement Work culture focused on innovative disruption Benefits may vary by location. Our Workplace While we prioritize a hybrid work environment, remote work may be considered for candidates located more than 30 miles from an office, based on role requirements for specialized skill sets. New hires will be invited to attend onboarding at one of our hubs within their first month. Teams also gather quarterly to support collaboration. Export Control Compliance This position requires access to export controlled information. To conform to U.S. Government export regulations applicable to that information, applicant must either be (A) a U.S. person, defined as a (i) U.S. citizen or national, (ii) U.S. lawful permanent resident (green card holder), (iii) refugee under 8 U.S.C. 1157, or (iv) asylee under 8 U.S.C. 1158, (B) eligible to access the export controlled information without a required export authorization, or (C) eligible and reasonably likely to obtain the required export authorization from the applicable U.S. government agency. CoreWeave may, for legitimate business reasons, decline to pursue any export licensing process. Equal Opportunity Employer CoreWeave is an equal opportunity employer, committed to fostering an inclusive and supportive workplace. All qualified applicants and candidates will receive consideration for employment without regard to race, color, religion, sex, disability, age, sexual orientation, gender identity, national origin, veteran status, or genetic information.

Senior Data Scientist AI for Science, Research Intelligence & Knowledge Discovery Technology - Data Science Organization Do you want to build advanced AI that helps researchers discover, understand, and advance science? Are you excited by the opportunity to design advanced AI systems that accelerate scientific discovery and unlock knowledge at scale? Would you enjoy building production ready solutions using machine learning, NLP, and generative AI to create meaningful impact for researchers and professionals? About Our Team Our global team supports products in education, electronic health records, and data discovery that introduce students to digital charting and prepare them to document care in today's modern clinical environment. We have a very stable product that we've worked to get to and strive to maintain. Our team values trust, respect, collaboration, agility, and quality. About the Role In this role, you will design and deliver advanced AI, NLP, and generative AI solutions that power knowledge discovery and decision support. You will work with complex scientific data and apply modern machine learning and LLM based approaches to build scalable, reliable systems with real user impact. You will also collaborate across teams to turn complex challenges into practical, production ready solutions. Responsibilities Design, build, and evaluate advanced AI/ML, NLP, and generative AI solutions for scientific and knowledge discovery applications. Develop LLM powered workflows and retrieval augmented generation (RAG) systems for search, summarization, question answering, and evidence grounded insight generation. Build intelligent retrieval, ranking, recommendation, and decision support capabilities using modern orchestration frameworks and AI techniques. Integrate scientific metadata, ontologies, taxonomies, and knowledge assets into scalable AI workflows. Establish robust evaluation, experimentation, and monitoring frameworks to ensure quality, trust, performance, and reliability. Write production ready Python code and partner with engineering teams to deploy solutions at scale. Provide technical leadership and mentoring to support high quality delivery and continuous improvement. Requirements Practical experience in data science, AI, machine learning, NLP, information retrieval, or a related quantitative field. Strong hands on experience building AI/ML, NLP, generative AI, and retrieval based systems in applied or product focused environments. Expertise working with LLMs, including fine tuning, prompt engineering, grounding strategies, and responsible AI practices. Strong Python skills and solid machine learning fundamentals. Experience working with large scale text or content rich datasets and modern AI/ML frameworks. Experience with RAG, semantic, vector, or hybrid search, along with experimentation and evaluation approaches that measure user impact. Familiarity with cloud platforms and modern software engineering practices. Strong communication, collaboration, and mentoring skills. Benefits & Working Pattern We promote a healthy work/life balance and offer flexible hours, allowing you to schedule work around your most productive times. Our wellbeing initiatives, shared parental leave, study assistance, and sabbaticals help you meet both immediate responsibilities and long term goals. Equal Opportunity Statement We are an equal opportunity employer: qualified applicants are considered for and treated during employment without regard to race, color, creed, religion, sex, national origin, citizenship status, disability status, protected veteran status, age, marital status, sexual orientation, gender identity, genetic information, or any other characteristic protected by law. USA Job Seekers: EEO Know Your Rights.

The Space Rangers team are recruiting a Senior System Engineer to join Splash Damage, at their onsite studio in Bromley! Location: London, Bromley (on-site) About Splash Damage: Based near London, Splash Damage is one of the UK's longest-running independent game development studios. For over two decades we've built team-based shooters that bring players together through cooperation, competition, and shared experiences. Our teams have contributed to some of the industry's biggest franchises, including Batman, Halo, and Gears of War, as well as creating original titles such as Brink and Dirty Bomb. Today the studio focuses on developing engaging multiplayer experiences in partnershipwith leading publishers, while continuing to invest in both new projects and our existingfranchises, including the open-world survival game SCUM. ABOUT THE ROLE: Systems engineers are part of the studios Core Tech team and are responsible for designing and implementing the core systems, pipelines and architecture that provide a solid framework for all our titles and working closely with other disciplines. As a Senior System Engineer, you will use your advanced knowledge and experience to own the technical design and implementation of large-scale system features that encompass rendering, networking, low-level systems, tools and build infrastructure, with a focus on scalability and performance. You will apply your expertise in data structures and algorithms for modern, multi-core architectures to build state-of-the-art technology for both PC and consoles. Through mentoring and championing of best practices, you will provide day-to-day support for more junior members of the team whilst effectively managing your own time and workload through delegation. You will help drive the project forward by working with your discipline lead to plan and maintain roadmaps. We believe that constantly improving on our skills is an essential part of the job and incorporate this into our daily projects. As a senior member of the Engineering team, you will help mentor and train team members. WHAT YOU'LL EXCEL AT: Writing clear, reliable, maintainable, performant, and portable code. Employing a can-do attitude to solve difficult problems as part of an agile, fast-moving, and highly-focused team. Mentoring more junior team members and providing clear and considered feedback. Identifying and championing best practices within your specialist domain. Interacting with both technical and non-technical colleagues from all disciplines. WHAT YOU'LL NEED TO SUCCEED: Professional Unreal Engine 5 development experience. Professional game engine code experience (in areas such as I/O, rendering, physics, memory management, multithreading, content pipelines, etc.). Professional experience in Systems development for Xbox Series S/X and/or PS5. Strong practical knowledge of C++ , with relevant professional experience. Strong understanding of computer science and good understanding of 3D maths . Experience with advanced usage of source control systems (e.g. Perforce). Experience with writing automation tasks. Good familiarity with profiling and optimising code for optimal CPU, GPU, memory and bandwidth usage. Good familiarity with debugging tools and techniques. Good familiarity with Unreal development ecosystem. Experience working in all phases of game development, from feature design and implementation to bug-fixing. Experience with and/or an appetite to explore what modern AI advances can bring to game development WHAT WE CAN OFFER: Competitive Salary and Benefits Package: Your health and wellbeing is important to us, so we offer a variety of benefits including: Enhanced Private Pension Scheme Private Medical Insurance Group Life Assurance Competitive holiday + studio wide closures in summer and winter Gym Membership Allowance Free Eye Tests Free on-site parking Electric Car Scheme Season Ticket Loans A FRIENDLY NOTE FROM THE RECRUITMENT TEAM: Let us do the work for you: Even if your profile isn't an exact match for all of the qualifications listed above, we still want you to apply. Our team members come from a variety of different industries, not all of which are immediately relevant to game or software development, and we welcome all candidates of similarly varied backgrounds, communities, and identities. Our client is an equal-opportunity employer. They believe their teams create better work when they have a range of perspectives to draw from, and we are committed to creating an inclusive working environment that celebrates diversity. 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Robotics Cloud Software Engineer Robotics Cloud Software Engineer III is a senior individual contributor responsible for architecting, developing, and scaling cloud services, dashboards, and data processing systems that support robotics and autonomous vehicle technology. This role combines cloud software engineering, distributed systems development, IoT platform integration, and hands on work with vehicles and robotic systems deployed in real world environments. Key Responsibilities Cloud Platform Development: design and develop scalable dashboards and user interfaces for robotics, traffic management and telemetry; integrate, transform, and combine multiple datasets to deliver meaningful, actionable user insights; use open source frameworks, tools, and libraries (NuxtJS, NestJS, Grafana, D3.js) to create visually rich and responsive dashboards; build cloud native APIs and microservices enabling machine to cloud communication using REST, gRPC, MQTT and messaging technologies; develop IoT solutions using Azure IoT Hub, Device Provisioning Service, Azure Event Hub, Azure Functions and other cloud services to support secure telemetry ingestion and bi directional device communication. Robotics and Telemetry Integration: integrate cloud services with vehicle and robotic systems, enabling telemetry streaming, remote monitoring, automation, and operator assist capabilities; build and maintain both real time and batch data ingestion pipelines using Azure Event Hub, Azure Stream Analytics, Azure Data Explorer and Cosmos DB; provide secure, reliable access for dealers and partners to HYMH databases via authenticated web service interfaces; collaborate closely with telemetry platform teams, robotics engineers, external vendors, and customer organizations to deliver end to end robotics functionality. Software Development, Testing and Reliability: simulate, verify and validate developed software to ensure it meets functional, integration and stakeholder requirements; deliver high quality software using Agile methodologies with an emphasis on reliability, scalability and system performance; implement observability using metrics, distributed logging and alerting (Azure Monitor, Application Insights, Prometheus, Grafana) to support high availability robotics solutions; contribute to engineering best practices, including documentation, coding standards, CI/CD pipelines and continuous improvement initiatives. Architecture and Technical Leadership: participate in architectural design and planning for cloud, data and robotics platform solutions, including IoT device management, telemetry architectures and data flows; mentor junior engineers and foster growth of technical expertise across the broader engineering team; contribute to sprint planning, design reviews, architecture sessions and cross functional technical discussions. Experience and Qualifications Bachelor's degree in electrical engineering, computer engineering, computer science or a related field. Four years of relevant engineering experience. Proficiency in at least one modern programming language such as Java, C++ or C#. Experience with SQL and NoSQL database development and automation of web queries. Experience working in an Agile software development environment. Experience with Microsoft Azure cloud services. Strong written and verbal communication skills. Strong analytical thinking, problem solving and design capabilities. Preferred Qualifications Exposure to robotics, autonomous vehicles, mobile or highway equipment, or industrial automation through coursework, internships, professional work or personal projects. Experience working with electrical engineering and embedded software teams. Experience with telemetry systems, IoT integration or fleet management platforms. Experience with ROS or ROS2, simulation environments or real time streaming data systems. Equal Opportunity Statement Hyster Yale Materials Handling, Inc. is an equal opportunity employer. The successful candidate will be subject to background checks.

About our team We are a fast-moving, high-impact Data Science & AI team building real-world GenAI and ML solutions across the entire LexisNexis business. Our work powers smarter decisions for Product, Sales, Finance, Marketing, Customer Success, and Engineering-everything from predictive models to enterprise GenAI apps to automation that transforms how teams operate. We are data science generalists who love variety. One day, it is designing a new GenAI workflow, the next it is deploying a model into Salesforce or engineering a pipeline in Databricks. We own our projects end-to-end and partner directly with stakeholders to deliver solutions that get used and make a measurable difference. If you want to experiment, build, ship, and see your work drive real impact across a global organisation, you will feel right at home with us. About the role We are seeking a Senior Data Scientist II who is a Data Science Generalist. The ideal candidate is comfortable working across GenAI, traditional machine learning, analytics, data engineering, cloud platforms, and enterprise system integrations. In this role, you will design, build, and deploy AI and ML solutions that support key business functions across Product, Sales, Finance, Marketing, Customer Success, and Engineering. You will work end-to-end across ideation, modelling, experimentation, prompt engineering, deployment, monitoring, and stakeholder communication. This position is ideal for a versatile data scientist who enjoys solving diverse problems, working with multiple systems, and driving measurable business impact. Responsibilities Build GenAI applications using OpenAI APIs, embeddings, vector search, and retrieval-augmented generation (RAG). Design advanced prompt engineering patterns and automated evaluation frameworks for LLM quality and safety. Develop and deploy traditional ML models (e.g., churn, propensity, sentiment/feedback, lead scoring, customer intelligence). Own the end-to-end model lifecycle: data prep, experimentation, deployment, and monitoring. Build and optimize feature pipelines and scoring jobs using Python, Databricks, Spark, Delta Lake, and AWS. Use AWS services (S3, Redshift, Lambda) for data automation, orchestration, and scalable processing. Ensure data quality, observability, lineage, and documentation across data and ML pipelines. Deliver enterprise integrations with Salesforce (SFDC) and Oracle platforms (Fusion, Service Cloud, Peoplesoft) for batch and real-time workflows. Create analytics solutions with cross-functional partners: define KPIs, connect customer/product/finance/CRM data, and drive actionable recommendations. Productionise reliably: provide L2/L3 support, monitor drift/data quality/prompt performance, run root-cause analysis, and implement preventative fixes. Requirements Strong Python programming skills. Direct experience with OpenAI APIs, LLM workflows, and prompt engineering. Solid machine learning fundamentals, including supervised learning, NLP, and feature engineering. Experience with Databricks, Spark, and Delta Lake. Strong SQL skills with experience working on large datasets. Experience with AWS, including S3 and Lambda. Familiarity with Redshift, Snowflake, or other cloud data warehouses. Experience with behavioral datasets. Ability to work across machine learning, data engineering, analytics, and integrations. Ability to design end-to-end solutions spanning data, models, APIs, and automation workflows.

Responsibilities Define and evolve the enterprise reference architecture for agentic and generative AI, establishing standards, decision principles, and guardrails that will shape how these capabilities scale across Equinor. Partner with AI and ML engineering teams to deliver production grade agentic systems: orchestration, grounded retrieval, structured LLM integrations with enterprise APIs, MCP based tool/data access, and multimodal document understanding. Lead solution strategy, technology choices, and architectural blueprints; align senior stakeholders, engineering teams, and strategic partners; and provide technical leadership from early concept through scaled deployment. Embed safety, compliance, and privacy by design; align with GDPR and the EU AI Act; enforce policy as code and safe tool execution; manage uncertainty in non deterministic systems by surfacing confidence, bounding autonomy, routing to human oversight, and providing safe fallback and rollback paths. Raise engineering quality and long term capability by driving performance, reliability, and cost efficiency while mentoring others and building reusable foundations for future AI solutions. Required Qualifications Master's or PhD in Computer Science, Data Science, Machine Learning, Linguistics, or related field. Deep architectural experience across data, model, and application layers, with strong judgment on trade offs, scalability, risk, and compliance in enterprise AI systems. Proven leadership in navigating ambiguity, shaping technical direction, aligning senior stakeholders, and translating AI strategy into business aligned execution. Hands on experience with modern LLMs and agent frameworks (e.g., LangGraph, AutoGen, LangChain/LlamaIndex, Semantic Kernel). NLP and generative AI expertise, including prompt design, RAG architectures, model evaluation, and practical experience with major LLM providers and open source models. Solid Python and software engineering fundamentals (testing, CI/CD, version control). A strong track record of delivering end to end AI solutions from concept to measurable business value. Preferred Qualifications Recognized thought leadership through mentoring, publishing, speaking, patents, or stewardship of influential open source initiatives. Multimodal AI: document and image understanding, diagram Q&A, speech to text (e.g., Whisper). Responsible AI: PII handling, red teaming, content moderation, risk assessment, regulatory compliance. Containers and cloud: Docker, Kubernetes; Azure (Azure ML, AKS, Azure OpenAI, storage, networking). Enterprise integration: APIs, events/messaging, standardised data and tool access via MCP. Benefits Comprehensive benefits package with competitive salary, global parental leave, bonus scheme and pension plan. Development opportunities: learning activities, internal job market for career growth across disciplines and geographies. Flexible work arrangements and work life balance support. Inclusive culture encouraging diversity, belonging, and respect. Equal Opportunities Equinor is an equal opportunity employer. We make all employment decisions without regard to race, colour, religion, sex, sexual orientation, gender identity, national origin, age, disability, marital status, parental status, veteran status, or any other protected status. Reasonable adjustments will be made during the recruitment process for candidates with disabilities or long term health conditions.

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.

The Product & Engineering team is responsible for designing, building and scaling Data & AI products that power intelligent experiences, decisions and actions across the Tesco ecosystem. Working in close partnership with business and technology teams across Stores, Finance, People, Customer and Commercial, we enable the seamless build, integration and adoption of Data & AI capabilities that deliver measurable value. This is an opportunity to play a leading role in shaping a new generation of Data & AI products, from problem definition through to launch and scale, driving meaningful impact for Tesco colleagues and the wider business. This is a hybrid role with the expectation of being based at our London and Welwyn Garden City offices 3 days a week. The Software Development Engineer III is a senior, hands on engineer specialising in building and operating GenAI powered products, including LLM based applications and agentic workflows that enable intelligent decision making and automation. The role spans the full product lifecycle, translating complex business problems into production grade solutions across data, backend services, APIs and user facing applications, with a strong focus on reliability, guardrails and real user impact. The role also provides technical leadership by designing shared GenAI capabilities, supporting live systems and raising engineering and AI delivery standards across the team through mentoring and rigorous design practice. Responsibilities Data & AI Product Development: Take ideas from discovery through rapid prototypes to production grade solutions that combine data, user facing applications and backend services. Work closely with product managers, designers, data science and engineering teams to translate business needs into well designed technical solutions. Design and deliver LLM based applications and AI agents that enable intelligent decision making and automation, applying pragmatic engineering patterns and appropriate guardrails. Design and consume APIs that support responsive, intuitive UIs, working across backend, data and frontend layers to deliver coherent, production-ready experiences. Build shared capabilities (libraries, templates, services, prompts/config patterns, evaluation suites) that reduce duplication and enable other squads to adopt GenAI safely and consistently. Support and operate live systems, troubleshoot complex issues, perform root cause analysis and continuously improve reliability, quality and speed of delivery. Build and maintain web-based user interfaces using React.js, modern JavaScript / TypeScript, HTML and CSS, enabling colleagues to interact effectively with data and AI based tools. Technical Excellence: Promote strong software engineering practices across backend and frontend development, including CI/CD, testing, secure by default development, performance optimisation and observability. Support and operate live systems, troubleshoot complex issues, perform root cause analysis and continuously improve reliability, quality and speed of delivery. Technical Leadership: Provide hands on technical leadership through design reviews, mentoring and knowledge sharing, helping raise engineering standards across the team. Qualifications Strong experience as a senior software engineer delivering and operating production systems. Solid backend engineering experience (e.g. Python or similar), including API design, testing, CI/CD and operational support. Practical experience building LLM-powered applications (e.g. copilots, assistants, agent based workflows), with a focus on reliability and real user impact. Good working knowledge of React.js and modern JavaScript / TypeScript, with experience building and maintaining production web interfaces. Proficiency with HTML and CSS, and an understanding of usability, accessibility and performance considerations. Experience working across data, backend and UI layers, collaborating with others rather than owning every layer in isolation. Ability to make thoughtful technical trade offs, communicate clearly with both technical and non technical stakeholders and work effectively in a product led team.

Why Socure? Socure is building the identity trust infrastructure for the digital economy - verifying 100% of good identities in real time and stopping fraud before it starts. The mission is big, the problems are complex, and the impact is felt by businesses, governments, and millions of people every day. We hire people who want that level of responsibility. People who move fast, think critically, act like owners, and care deeply about solving customer problems with precision. If you want predictability or narrow scope, this won't be your place. If you want to help build the future of identity with a team that holds a high bar for itself - keep reading. About the Role The Big Data R&D team is responsible for building the core identity graph and entity-resolution capabilities that power Socure's KYC and compliance products. In this role, you will help develop graph-based algorithms and data pipelines on massive PII datasets, support modelers with high-quality features, and evaluate new data sources that feed our identity and fraud products. You will work closely with senior data scientists and engineers while developing your skills in large-scale ML, distributed systems, and graph analytics. What You'll Do Contribute to the design and implementation of machine learning, data mining, statistical, and graph-based algorithms to analyze very large datasets for identity verification and anomaly detection. Analyze large datasets to help develop and refine entity-resolution and identity-matching algorithms that drive Socure's KYC and compliance solutions. Build and maintain components of data-processing pipelines (ETL, feature generation, normalization) using tools such as Spark/PySpark and AWS (e.g., EMR, S3). Support senior data scientists with feature engineering, data exploration, error analysis, and A/B test setup for new models and signals. Help evaluate new third party and internal data sources: profile data quality, design offline experiments, and summarize impact on coverage and model performance. Implement and maintain SQL and Python/R code for data extraction, transformation, and validation; contribute to code reviews and basic testing. Provide analytical support to compliance and regulatory product teams, including ad hoc investigations, simple dashboards, and data deep dives. Communicate findings in a clear, structured way to peers and cross functional partners (Product, Engineering, Client Analysis), focusing on key insights and trade offs. Work effectively in a fast paced, cross functional environment; demonstrate ownership of well-scoped tasks and follow through to completion. What You Bring Master's degree with 2+ years of experience, or Ph.D. with 1+ years of experience in a data science or analytics role, or equivalent practical experience. Proficiency in at least one general-purpose programming language used in data science (Python, or Scala). Solid experience writing and optimizing SQL for large datasets; comfort working in data lake / warehouse environments. Hands on experience with Spark or PySpark and common ML libraries (e.g., scikit learn, XGBoost, TensorFlow/PyTorch a plus). Familiarity with UNIX environments and the AWS ecosystem (e.g., EMR, S3); Databricks experience is a plus. Working knowledge of supervised/unsupervised ML and basic statistics (similarity measures, clustering, evaluation metrics). Exposure to graph techniques or graph databases (Neo4j, AWS Neptune, GraphFrames) is a strong plus. Bonus: experience with Elasticsearch or DynamoDB; workflow tools such as Airflow for automating data pipelines. Ability to break down loosely defined problems, ask good clarifying questions, and iterate quickly with feedback. Note: sponsorship is not available at this time; and that you must be located within 45 miles of a talent hub to be considered. Socure is an equal opportunity employer that values diversity in all its forms within our company. We do not discriminate based on race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status. If you need an accommodation during any stage of the application or hiring process-including interview or onboarding support-please reach out to your Socure recruiting partner directly.

Why Socure? Socure is building the identity trust infrastructure for the digital economy - verifying 100% of good identities in real time and stopping fraud before it starts. The mission is big, the problems are complex, and the impact is felt by businesses, governments, and millions of people every day. We hire people who want that level of responsibility. People who move fast, think critically, act like owners, and care deeply about solving customer problems with precision. If you want predictability or narrow scope, this won't be your place. If you want to help build the future of identity with a team that holds a high bar for itself - keep reading. About the Role The Big Data R&D team develops cutting edge big data and graph based solutions for entity search, entity resolution, and identity matching that power Socure's KYC and compliance products. As a Senior Data Scientist I, you will lead the design and deployment of advanced ML and graph algorithms on large-scale PII datasets, own end to end projects from problem definition through production validation, and serve as a key technical partner to Product, Engineering, and Client facing teams. You will help define standards for feature engineering, experimentation, and data quality across our identity graph stack, with substantial impact on coverage, accuracy, and fairness. What You'll Do Own the design, development, and evaluation of machine learning, statistical, and graph-based algorithms for entity-resolution, identity trust scoring, and anomaly detection on massive datasets. Architect and optimize graph-based identity representations (identity graph structure, linkage rules, clustering) to improve match rates, reduce false positives/negatives, and support downstream fraud and KYC models. Build and maintain scalable data pipelines and feature stores in Spark/PySpark (or Scala), including data normalization, deduplication, and feature computation across large PII datasets in AWS/Databricks environments. Lead A/B tests and offline/online experimentation for new models, features, and data sources; define success metrics, design experiments, and ensure rigorous validation before rollout. Evaluate new internal and external data sources: explore signal quality, design backtests, quantify incremental value, and provide clear recommendations on vendor selection and integration. Partner closely with product managers and engineers to translate ambiguous business and regulatory requirements (e.g., KYC coverage, watchlist matching) into concrete modeling and data roadmaps. Provide deep analytical support to Socure's compliance and regulatory product suite, including investigative analyses, root cause analysis for anomalies, and clear narratives for internal and external stakeholders. Contribute to model governance and documentation: clearly explain model logic, data dependencies, limitations, and monitoring plans to internal risk/compliance stakeholders. Mentor junior data scientists and engineers on best practices in data exploration, feature engineering, experimentation, and code quality. Communicate complex technical concepts and trade offs in a concise, structured way to both technical and non technical audiences (e.g., product reviews, customer meetings, internal briefings). What You Bring Master's degree with 3+ years of relevant industry experience, or Ph.D. with 1+ years of experience in applied ML / data science roles; background in Computer Science, Statistics, Mathematics, or related quantitative fields preferred. Strong proficiency in Python (preferred) or Scala, including experience with ML libraries such as scikit learn, XGBoost, TensorFlow or PyTorch. Extensive experience with Spark or PySpark and distributed data systems (e.g., AWS EMR, Databricks) working on very large, messy datasets. Deep understanding of supervised and unsupervised learning, feature engineering, model evaluation, and experiment design (A/B testing, holdout strategies, stratification). Experience developing production-quality data pipelines and automated workflows using Airflow or similar orchestration tools. Practical familiarity with graph databases and/or graph frameworks (Neo4j, AWS Neptune, GraphFrames, DGL, PyTorch Geometric) and graph algorithms for clustering, link prediction, and community detection is strongly preferred. Solid SQL skills and experience working with large-scale analytical data stores. Experience in at least one of: identity verification, fraud detection, credit risk, or adjacent high stakes domains is a plus. Demonstrated ability to lead medium to large projects end to end, make sound trade off decisions under ambiguity, and influence cross functional stakeholders with data and clear reasoning. Socure is an equal opportunity employer that values diversity in all its forms within our company. We do not discriminate based on race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status. If you need an accommodation during any stage of the application or hiring process-including interview or onboarding support-please reach out to your Socure recruiting partner directly. Please note that sponsorship is not available at this time; and that you must be located within 45 miles of a talent hub to be considered.

Position Overview Proximie's engineering organization is the heartbeat of the company - designing and delivering products that drive measurable clinical and operational impact. Our platform empowers surgical teams around the world to improve patient outcomes, optimise operating room efficiency, and harness data for smarter, more connected surgery. As Proximie continues its global expansion, we are seeking a Senior Data Engineer to join our data engineering team. Our data has become more important than ever. We need to utilise it to make analytical decisions, store it securely, move it between different systems, all the while ensuring that we adhere to healthcare and data protection regulations. As a Senior Data Engineer you'll help shape the future of Proximie's Data & Insights platform, ensuring our systems are robust, secure, and scalable to power analytics, AI, and data products used by hospitals worldwide. Responsibilities Reporting to a Data Engineering Lead based in the UK, the Senior Data Engineer will be responsible for: Design, evolve and future proof scalable data architectures to meet performance, cost, and reliability goals. Build and orchestrate resilient batch and real time data pipelines. Data modelling & cataloguing including: Performance tuning in various technologies (e.g. Spark, Presto, Athena, Postgres) Build automated testing frameworks including implement anomaly detection and validation checks (e.g. with Great Expectations). Monitoring & Incident Response including: Enforce data access controls, encryption at rest/in transit, PII masking/anonymization, and collaborate on HIPAA/GDPR (or equivalent) compliance. Manage and version cloud infrastructure using IaC tooling (e.g. Terraform) Collaborate closely with Data Scientists, Analysts, Product, and Customer Success teams to design reliable, well documented datasets and APIs that enable analytics, insights, and ML capabilities. Drive development of customer facing data products - APIs, timelines, dashboards - and support machine learning data pipelines. Mentor junior engineers, champion best practices, document architectures/runbooks/data dictionaries, and standardise coding conventions and CI/CD. Requirements Bachelor's degree in Computer Science, Mathematics, or related field (or equivalent practical experience). Minimum of 5+ years as a Data Engineer with experience developing and supporting with data lakes, warehouses preferably on AWS. Extensive experience designing, deploying, and maintaining data infrastructure on AWS (or equivalent), including data lakes, warehouses, and real time pipelines. Experience across a variety of AWS products such as S3, Glue, EMR, Lambda, Aurora or equivalents. Professional software development experience with Python and SQL. Able to work as a team to deliver against requirements in a collaborative and agile way. Desirable Requirements Certification from a major cloud provider, ideally AWS Certified Solutions Architect. Data visualisation experience. Experience of testing, CI and monitoring data driven applications. Experience working with third party APIs. Experience with dbt, Snowflake or DataBricks. Experience with orchestration tools e.g., Airflow. Experience with messaging and event processing platforms. Building REST/GraphQL APIs. Benefits You will be encouraged to grow in your role, take ownership and gain responsibilities. Proximie's values are Ownership, Deliver Results, Build Trust and Go Beyond. Generous annual leave. Two "well being" days per year plus the day off for your birthday. "Summer Fridays" - early office closing on Fridays during summer months. Annual bonus programme - based on individual contribution. To support your professional growth, all permanent employees will have access to an annual stipend of £1,000 to assist with personal development activities. Flexible working hours - we trust our people to manage their time and to focus on wider results. A flat organisational structure where every opinion matters, ideas are cultivated, and innovation is encouraged. Proximie is a truly global company with teams across the UK, Europe, United States, and the Middle East where you will have opportunities to see the world. Proximie is an equal opportunity employer. We are committed to providing a work environment that supports, inspires, and respects all individuals. We do not discriminate on the basis of race, colour, religion, marital status, age, national origin, ancestry, physical or mental disability, medical condition, pregnancy, genetic information, gender, sexual orientation, gender identity or expression, veteran status, or any other status protected under the law.