Machine Learning Jobs

Amazon · Cambridge

Joins a research team advancing multimodal deep learning for consumer devices, focusing on dataset curation, annotation pipelines and automated evaluation methods such as fine-tuned LLM-as-judge. Analyses large offline and online datasets in Python to diagnose model failures and works with training, compiler and product teams to move models from prototype to production.

Wayve · London

Develops, tunes and validates trajectory-generation and motion control algorithms that translate the AI Driver's outputs into safe, smooth vehicle motion across ADAS and AV platforms. Writes latency-sensitive, production-quality C++ control software and uses simulation, closed-loop evaluation and on-road testing to characterise controller behaviour and debug system-level issues.

Cohere · London

Research engineering role on the integration team, building and scaling machine learning algorithms and infrastructure for LLM post-training, with a focus on large-scale distributed RL methods. Day to day involves writing high-performance training software, developing research tooling, and collaborating with infrastructure, efficiency, serving and scientific teams to improve SFT, offline preference and RL training cycles.

Amazon · London

PhD students join an Amazon Science team to own an end-to-end research project, from scoping with a mentor to deploying algorithms, models or systems into production. Projects span computer vision for Prime Video quality, multimodal AI for Rufus, automated reasoning and formal verification, observability for distributed systems, and recommendation models. Interns may publish at top-tier conferences or see their work ship before the internship ends.

NVIDIA · Bristol

Research and develop techniques to GPU-accelerate leading high performance computing applications across scientific computing, computational engineering and data science. The intern performs in-depth analysis and optimisation for current and next-generation GPU architectures, working with application developers and collaborating with architecture, research, libraries, tools and system software teams.

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.

NVIDIA · Zürich, Switzerland

Research and develop techniques to GPU-accelerate leading high performance computing applications across scientific computing, computational engineering and data science. The intern performs in-depth performance analysis and optimisation for current and next-generation GPU architectures, working directly with application developers and collaborating with architecture, research, libraries, tools and system software teams.

TTP · Cambridge

Consultants design and deploy machine learning systems for diverse defence and security projects, from proofs-of-concept to working prototypes. The role spans literature analysis, model training, rapid prototyping and experiment design, often combining science, engineering and ML in novel settings. Work is on-site in Cambridge within a flat, collaborative team of around 40 specialists.

Cohere · London

Own end-to-end technical program delivery for UK public sector and defence customers, from initial deployment through production scaling, acting as the bridge between frontier AI capabilities and government requirements. Navigate procurement, security authorisations, data sovereignty and compliance while coordinating Applied ML, Forward Deployed Engineering, Platform and Product teams. Work spans air-gapped, on-premises and highly regulated environments.