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.

PhysicsX · New York, United States · US$150,000 – US$250,000 pa

Customer-facing role owning the full customer journey, from onboarding and change management to roadmap planning and contract preparation. Works alongside simulation, software, ML engineers and data scientists to translate industrial engineering challenges into product opportunities, tracking engagement and measuring value. Hybrid role based in Manhattan with remote flexibility.

PhysicsX · New York, United States · US$240,000 – US$300,000 pa

Leads regional deployment engineering for a physics AI platform, owning end-to-end delivery into customer-owned infrastructure and acting as primary technical contact for CIOs and CTOs. Combines executive engagement with hands-on work on Kubernetes, cloud and infrastructure-as-code deployments, while hiring and scaling a team of deployment engineers. Requires navigating regulated, high-security and on-prem/HPC environments.

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

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.

Quantexa · London

Builds and shapes next-generation data analytics products within the R&D team, working on the core Contextual Decision Intelligence platform used for fraud, financial crime and data management. Back-end services are written in Scala as microservices, using Cats, Shapeless, Akka and Monix, with event sourcing and CQRS in the persistence layer.

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.

Cohere · London

Designs next-generation evaluation benchmarks and infrastructure to measure LLM progress, including training LLM judges, refining data synthesis pipelines, and improving evaluation efficiency. Works cross-functionally to turn model feedback into trustworthy, repeatable evaluations, and builds scalable tools for probing model performance.

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.