Machine Learning Jobs

• New York, United States
• Hybrid
• US$150,000 – US$250,000 pa
• Lead

Customer-facing role owning the full journey from onboarding and change management to roadmap planning and contract preparation, working alongside simulation, software, ML engineers and data scientists. Translates complex engineering and physics challenges into product opportunities, tracks engagement and grows strategic accounts. Hybrid, based in NYC or the Bay Area.

• London

Builds and owns the LLM access layer for agents on the PhysicsX platform, serving self-hosted open models and routing each task to the best-fitting model or combination. Sets technical direction for inference engines like vLLM and SGLang and serving platforms such as KServe, including air-gapped deployments, GPU autoscaling, batching, caching and quantisation. Works with Research and Post-Training specialists to move tuned models into production routes.

vLLM · SGLang · KServe +6

• London
• Senior

Founding engineer role building the Lakebase managed Postgres transactional layer, spanning control plane, proxy data path, and platform reliability. Work covers large-scale distributed systems, high-throughput connection routing, SLI/SLO definition, and AI-powered automation across AWS, GCS, and Azure storage backends.

AWS S3 · GCS · Azure Blob Storage +6

• London
• Remote
• £75,000 – £105,000 pa
• Senior

Design, build and deploy production-grade LLM applications, RAG solutions and scalable data pipelines for enterprise clients at a growing technology consultancy. The role spans software engineering, data engineering and AI delivery, with a focus on taking solutions from proof of concept into production rather than research.

Databricks · MLflow · LangChain +5

• London

Embed with strategic enterprise customers to design, build and productionise custom fullstack data and AI applications on the Databricks platform. You own architecture and end-to-end delivery across data engineering, ML/AI integration and user-facing interfaces, partnering with Sales, Product and Field Engineering. Travel to client sites up to 50%.

Databricks · OpenAI · Anthropic +7

• London
• Hybrid
• Permanent

Customer-facing technical role helping organisations evaluate, build, deploy and adopt AI technology. Work spans architecture, integrations, data, security and evaluation, plus demos, prototypes and production solutions, with enablement workshops and delivery coordination alongside Sales, Product, Research and Engineering. This is a general talent pool rather than a specific vacancy.

OpenAI

• London
• Hybrid
• Lead
• Permanent

Lead the technical direction for an offline 3D reconstruction system that turns raw vehicle sensor data into quality-assessed 3D ground-truth geometry and ego motion. The role spans SLAM, lidar point-cloud registration, state estimation and sensor fusion, with responsibility for architecture, roadmaps, interfaces and cross-team alignment. It is a principal-level leadership position mentoring engineers and setting reconstruction quality standards.

ROS · PCL · OpenCV +3

• London
• Hybrid · 3 days in office
• Mid-level
• Permanent

Builds internal tools and data pipelines for lawful data access request workflows and Legal Operations, covering intake, scoping, authorized retrieval, review and audit. Involves backend services, least-privilege access, approval gates, provenance and audit trails, working closely with Legal, Privacy and Security stakeholders.

• London
• Hybrid
• Senior
• Permanent

Owns the technical architecture and roadmap for LiDAR and cross-sensor 3D sensing, leading development of target-based and targetless calibration, LiDAR-to-camera alignment, temporal synchronisation and motion compensation. A senior individual-contributor role combining algorithm development, evaluation and production deployment with technical leadership and mentorship across the 3D sensing stack.

GTSAM · Ceres Solver · g2o +1

• Manchester

The OpportunityA leading financial consultancy is seeking a Senior AI & ML Engineer to lead the design, development and deployment of cutting-edge AI, Machine Learning and Generative AI solutions. You'll play a key role in shaping technical strategy, building production-grade…

• United Kingdom
• Hybrid · 1 day in office
• £90,000 – £100,000 pa
• Lead
• Permanent

Hands-on technical lead for a large-scale AI programme, turning architectural direction into production-grade systems across complex legacy and multi-cloud environments. Builds RAG pipelines, semantic search, embedding infrastructure and orchestration layers, sets coding standards, mentors engineers and embeds Responsible AI guardrails, security and observability.

LangChain · LlamaIndex · AWS +3

• Oxford
• On-site
• £39,424 – £47,779 pa
• Entry level
• Contract

Develops novel statistical and machine learning methods for large-scale genomic, phenotypic and health data, covering areas such as genomic language models, multimodal and federated learning, and multi-ancestry methods. The role involves building scalable, reproducible research software, applying it to biobank-scale datasets, publishing findings and guiding junior researchers within the Palamara Group.

• Royston
• £58,000 – £60,000 pa
• Senior

Owns AI security initiatives within a Cyber Security and Compliance team, evaluating and integrating AI security tooling and defending against threats like prompt injection, data leakage and model poisoning. Also builds AI-driven detection, vulnerability management and incident response, running threat modelling and adversarial testing of AI systems.

OWASP Top 10 for LLM Applications · MITRE ATLAS · NIST AI RMF +1

• Cambridge
• Senior

Design and optimise CPU compilers for NVIDIA's Grace CPUs, working on hardware-software co-design, code generation and performance analysis for AI, HPC and data centre workloads. The role involves contributing features and optimisation techniques to upstream open source projects such as GCC, LLVM or MLIR, alongside a geographically distributed compiler team.

GCC · LLVM · MLIR +7

• London
• Hybrid
• Senior
• Permanent

Architect novel generative models to design functional proteins, working in an interdisciplinary team of ML researchers, protein engineers and biologists. The role spans data curation, evaluation metrics, rapid prototyping, cloud training and inference optimisation, plus planning wet lab testing campaigns with the bio team.