Machine Learning Jobs

• 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
• 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

• London
• Senior
• Contract

Builds ML and graph-based identity resolution where rule-based matching falls short, improving customer-spine architecture and reducing identity drift. Defines matching-quality metrics, supports golden-layer integration and delivers a reusable identity-resolution framework. Requires entity resolution, fuzzy matching, MLOps and feature-store skills plus Customer Master/MDM experience.

MDM

• Cambridge
• Mid-level

Hands-on compiler engineering focused on kernel generation and computational graph optimizations for next-generation NVIDIA GPUs, solving complex compilation problems for AI inference and training workloads. Works with MLIR, C/C++ and Python, collaborating with software, hardware and research teams on hardware/software co-design and datacenter-scale AI deployment.

MLIR · GPU · CPU +1

• United Kingdom
• Remote

Hands-on development of kernel generation and computational graph optimizations for next-generation NVIDIA GPUs, solving complex compilation problems for AI inference and training workloads. The role involves hardware/software co-design with software, hardware and research teams, and scaling datacenter AI deployments, with heavy use of MLIR, C/C++ and Python.

MLIR · GPU · CPU +1

• San Francisco, United States
• Hybrid
• US$150,000 – US$190,000 pa

Delivery-focused role embedding physics-aware AI models into customer tools, building reliable ML data pipelines and working with 3D point-cloud and mesh data. Spans data science and software engineering, translating R&D into reusable libraries and products, with regular on-site customer travel.

TensorFlow · MLflow · Spark +6

• New York, United States
• Hybrid
• US$150,000 – US$190,000 pa
• Mid-level

Builds and deploys machine learning systems for industrial physics challenges, designing reliable data pipelines and working with 3D point-cloud and mesh data. Partners directly with customers on-site to embed AI models into practical tools, translating R&D outputs into reusable libraries and products. Requires Python, cloud, and MLOps experience with frequent travel.

TensorFlow · MLflow · Spark +6

• Cambridge
• Hybrid
• Mid-level

Design and build LLM-agentic workflows that reason over knowledge graphs, proprietary methods and scientific literature to generate and triage therapeutic hypotheses for rare diseases. Works closely with drug discovery scientists in a cross-functional team, using Python, ADK and AgentSpace, with scope to own workflows end to end.

Python · ADK · AgentSpace

• London
• On-site
• £150,000 – £175,000 pa
• Senior

Hands-on role building LLM agent and retrieval systems on a small UK core team, reporting to the Head of Engineering. Work spans LangGraph/LangChain workflows, RAG pipelines with Qdrant and GraphRAG on Neo4j, model integration across Anthropic, OpenAI and Bedrock, plus evaluation harnesses and observability. Token cost, latency and auditability are treated as engineering constraints throughout.

LangGraph · LangChain · FastAPI +7

• Exeter
• £21,805 pa
• Entry level
• Contract

Builds next-generation machine learning models to predict genetic interactions from large-scale perturb-seq data, evaluating graph neural networks and autoencoders for genome-wide scalability and cross-cancer generalisation. Works in a lab-in-the-loop cycle with AstraZeneca and Cancer Research Horizons FGC, who generate pancreatic cancer perturb-seq data to fine-tune the computational models. Purely computational, with high performance computing and industry-aligned experience.

Perturb-seq · DepMap · Project Score

• London
• Hybrid · 3 days in office
• Senior · manages people

Lead and contribute to core machine learning research applying large language models to drug discovery, developing and refining LLM-driven approaches across biology and chemistry problems. Work spans post-training, reasoning, test-time scaling, tool-use, alignment, agents and reinforcement learning, with model training and inference engineering in JAX, PyTorch or TensorFlow. Depending on experience, you may also lead standalone projects, mentor ML researchers and shape the research roadmap.

JAX · PyTorch · TensorFlow +3

• London
• Hybrid · 3 days in office
• Senior · manages people

Builds greenfield machine learning models and algorithms for drug discovery, designing new architectures, running experiments and scaling training and inference frameworks. Works in an interdisciplinary team of ML scientists, engineers and domain experts, with opportunities to lead projects and mentor others depending on experience.

JAX · PyTorch · TensorFlow +3

• London
• Mid-level
• Permanent

Hands-on analysis and modelling of large multimodal datasets to measure and improve Rufus, Amazon's conversational shopping assistant. Designs and analyses A/B tests, builds predictive models and metrics dashboards, and works with NLP, information retrieval and recommender systems alongside scientists and engineers in a London team of around 150.

Large Language Models · Knowledge Graphs · ETL

• United Kingdom
• Entry level

Graduate role in the Human-Machine Understanding group, working at the intersection of AI, cognitive science and human-machine interaction. You will contribute to an Intelligent Interfaces framework spanning multi-modal human sensing, human-centric contextual modelling and agentic workflows, supporting client projects from human subjects studies to technical prototypes. Requires Python and AI/ML frameworks such as PyTorch, with scope to explore HRI, affective computing and cognitive architectures.

PyTorch · ROS2