Machine Learning Jobs in York

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.

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.

Cohere · London

Research scientists and engineers advancing multilingual large language models, designing scalable solutions to improve LLM performance across many languages and skills. The role spans building ML pipelines, publishing research at top-tier venues, and mentoring junior team members, with a strong emphasis on Python and NLP fundamentals.

Cohere · London

Builds and owns the training framework powering frontier-scale language models, spanning distributed training abstractions, checkpointing, and performance profiling across thousands of GPUs. Works at the intersection of large-scale training, distributed systems, and HPC, partnering with infra, research, and deployment teams on tooling that speeds up ML development.

Cohere · London

Builds and optimizes the synthetic data pipeline behind Cohere's frontier language models, running scalable inference on large GPU clusters and transforming web and code data with generative models. Involves data ablations, mixture experiments and model evaluation to improve token efficiency and training throughput, working closely with researchers and engineers.

Cohere · London

Interns work across frontend, backend, full-stack or infrastructure teams, shipping user-facing product code, building API platform features, designing data pipelines and scalable ML infrastructure, and contributing to CI/CD tooling and technical documentation. Interns are treated much like full-time engineers, with autonomy and ownership over production code, supported by mentors and team leads.

Cohere · London

Research internship focused on building and training large language models, with projects spanning base model training, retrieval augmented generation, evaluation, safety, and finetuning. Interns design and implement novel research ideas, ship state-of-the-art models to production, and publish results through papers, datasets, and code.

Cohere · London

Owns key components of a large-scale web data pipeline for pre-training language models, covering extraction, parsing, deduplication and filtering of noisy internet corpora. Analyses data composition and quality across domains, languages and time periods, and studies its impact on downstream model performance alongside research and evaluation teams.

Cohere · London

Founding Solutions Architect for the Nordics, owning the technical relationship with enterprise and government customers. Leads design and deployment of pilots, maps customer problems to AI solutions, and works with account executives, partners and product teams to drive adoption of LLM technology.

Cohere · United Kingdom

Interns design, train and improve cutting-edge models, help develop techniques to train and serve models safer and faster, and work on extremely large-scale training over massive datasets. They explore continual and active learning for streaming data, collaborate with product teams, and learn from senior ML staff. The role is a full-time 3-6 month internship, co-op or research work term for currently enrolled students.

Cohere · London

Build and ship features for North, Cohere's secure AI workspace platform, developing autonomous agents that interact with sensitive enterprise data. Work spans Python and React full-stack code, with stringent deployment constraints that often require reinventing tooling rather than using popular libraries, plus collaboration with researchers to productionize frontier models.