Machine Learning Jobs in York

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.

ElevenLabs · London

Builds and operates the platform every team ships on, running Kubernetes clusters including GPU fleets for training and inference across cloud and on-prem. Sets up compute, networking, IAM and data systems for other teams, and writes Python or Go tooling to make infrastructure self-serve. Works hands-on in a flat team on cluster builds and cloud migrations.

ElevenLabs · United Kingdom

Owns the data infrastructure behind frontier AI models, building large-scale pipelines for collecting, filtering and transforming training datasets. Also trains classifiers, quality filters and labeling models, and designs curation strategies like deduplication and quality scoring. Fully remote, with tooling work letting researchers explore and train on massive datasets.

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

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.

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

Owns the evaluation strategy for an enterprise AI workspace, building evals that capture agentic workflows, tool use and knowledge work. Translates user feedback, production failures and usage logs into continuous measurement, then feeds model failures and capability gaps back to central modelling teams. Sits between product, customer-facing and research teams rather than being pure research or product engineering.

Cohere · London

Builds reinforcement learning environments for enterprise AI agents, populating them with hard tasks, plausible data and reward verifiers, then runs agents inside to evaluate and train them. Works across modelling and product to find capability gaps, and partners with external vendors on expert-built environments and quality tooling.

Cohere · London

Designs and writes high-performance, scalable software for training frontier models, improving the training setup from an infrastructure and codebase performance standpoint. Builds tooling to speed up training cycles and works on supercompute and data infrastructure, bridging research and production. Works with distributed training stacks like Kubernetes, Slurm and Ray, with one of the highest compute-to-engineer ratios in the world.

Cohere · London

Builds and maintains the data pipeline behind Cohere's large language models, running data ablations to assess quality and designing pre-training data mixtures that improve model performance. Combines research and engineering, working with Python, Spark or Beam on web, code and multilingual corpora alongside researchers and engineers.

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.

Cohere · London

Forward deployed engineer building and shipping LLM-powered agentic workflows for enterprise customers, from early prototypes to production-grade agents. Works directly with clients in finance, healthcare and telecoms, integrating models with tools, APIs and sensitive data, and contributing features to Cohere's North AI workspace platform.

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.