Machine Learning Jobs

CureAge Therapeutics · London

Lead the computationally guided design of a lipid nanoparticle delivery platform, applying chemical and biophysical principles to optimise LNP composition and formulation. Use AI-enabled chemistry, cheminformatics, predictive models and molecular modelling to prioritise formulation components, working closely with the experimental team and founding team in a small start-up.

Hadean · London

Lead a new AI-powered defence product team, line-managing AI/ML, simulation, infrastructure and web engineers while staying hands-on in TypeScript, React and Node. You own delivery end-to-end, hiring and people development, partnering with Product and technical leads, with hybrid working one day a week in Shoreditch.

NVIDIA · United Kingdom

As a Senior Quantum Algorithm Researcher, you will lead technical collaborations with supercomputing centers and industry partners to develop hybrid quantum-classical applications. Your role involves driving research in AI for quantum algorithms, contributing to strategic projects, and publishing impactful research while representing NVIDIA at conferences.

Wayve · London

Lead a small team iterating on Wayve's end-to-end L2 driving model, balancing hands-on Python coding and experiments with directing priorities. Diagnose on-road issues, root-cause them, and drive fixes through data recipes, model architecture, evaluation and release readiness, working across teams on model and vehicle interface problems.

Humanoid · London

Develops learning-based locomotion and whole-body control policies for humanoid robots, using reinforcement and imitation learning, generative models and motion primitives. Builds scalable simulation and training pipelines in Isaac Lab and MuJoCo, and deploys, debugs and fine-tunes policies on physical robots, tackling sim-to-real transfer and real-time inference latency.

Amazon · London

Lead the science vision for Trusted Supply, safeguarding advertiser trust across Amazon Advertising by developing algorithms for brand safety, suitability scoring, fraud detection, viewability prediction and privacy-preserving measurement. The role spans multi-year research direction, publication roadmaps, cross-team modelling frameworks and production systems serving billions of ad impressions daily, alongside hiring and mentoring applied scientists and research engineers.

Reed · Cambridge

Lead statistical and bioinformatics analysis projects, developing computational algorithms and applying machine learning to functional genomics research. Work with NGS, OMICS and high-throughput datasets alongside laboratory scientists, using Python, R or Julia and libraries such as TensorFlow and PyTorch. The role also involves mentoring and cross-functional collaboration in a multidisciplinary research team.

Wayve · London

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.

Cambridge Consultants · United Kingdom

Work as part of a multi-disciplinary technical team on client assignments spanning consumer devices, industrial systems, and defence. Design and implement embedded systems, including C software for embedded targets, SystemVerilog for FPGAs, PCB design, and hardware testing, while interacting directly with clients from requirements capture to handover.

Relation Therapeutics · London

Processes and QC's paired single-cell RNA-seq and scATAC-seq at scale, building Nextflow pipelines and multimodal training datasets in Zarr for GPU-accelerated model training. Partners with wet lab scientists and ML modellers, contributing to evaluation frameworks that test whether models capture meaningful biology.

Amazon · Edinburgh

Builds machine learning recommendation systems that match job seekers to roles and recruiters to talent across Amazon Jobs and internal hiring platforms. Works on representation learning, reinforcement learning and probabilistic modelling at global scale, with a focus on fairness and explainability, alongside engineers, scientists and product managers through the full development lifecycle.

Anthropic · London · £370,000 – £630,000 pa

Builds and improves the core RL training infrastructure that researchers depend on, profiling, debugging and rearchitecting bottlenecks across the stack. Works closely with researchers and adjacent teams such as inference and sandboxing to ship tooling that speeds up training runs, spanning low-level performance work through to RL algorithms.

Faculty · London

Lead high-impact AI projects for retail and consumer clients, defining data science approaches, designing software architectures, and mentoring colleagues. Own the technical side of client relationships, scope projects, and embed responsible AI practices while delivering bespoke algorithms and production-grade systems.

Cohere · London

Hands-on research and engineering role on the Agent Code team, developing code-generating LLMs and autonomous agent systems for enterprise use. Involves training code models, building agent frameworks for inference and sampling, creating SFT trajectories, working on RL algorithms, and designing benchmarks, in collaboration with the pretraining team on frontier compute infrastructure.

Cohere · London

Drive safety research for agentic LLMs, focusing on data generation, post-training algorithms and evaluation methods so models that access external tools and act in the world remain fair, trustworthy and secure. Work with cross-functional ML, data annotation, product and policy teams on a small, autonomous team, requiring strong statistics, software engineering and Python/PyTorch skills.

Relation Therapeutics · London

Builds and scales generative and predictive models of cellular behaviour, acting as the bridge between ML theory and implementation. Day to day involves optimising large neural networks, distributed multi-GPU/multi-node training, profiling GPU workloads, and productionising research code for multi-modal biological data.