Machine Learning Jobs in London

• London
• Hybrid · 3 days in office
• Lead

Lead medicinal chemistry strategy across drug discovery programmes, from hit identification through lead optimisation to candidate nomination, while working hands-on as an individual contributor. Partner closely with AI/ML platform teams to shape predictive models, manage CRO resources and represent the company externally.

AlphaFold

• London
• Hybrid · 3 days in office
• Lead

Builds and evolves a large-scale ML infrastructure platform that supports training and deploying novel models across vast compute and data pools. Works closely with engineering, research and product teams, and explores deploying agents to improve platform robustness. Requires strong Python and experience scaling complex systems, with ML or Kubernetes exposure a plus.

Kubernetes · Google Cloud Platform · GCP

• London
• Hybrid
• Senior · manages people

Owns research work-streams, setting technical direction and roadmaps while guiding junior team members. Builds deep learning and probabilistic models of physical systems, translating physics and engineering problems into mathematical formulations and production-ready code with ML and simulation engineers.

PyTorch · CUDA · JAX +2

• London

Customer-facing AI engineering role delivering professional services engagements, helping clients build and productionize GenAI applications such as RAG, multi-agent systems and Text2SQL. Works cross-functionally with product and engineering to shape roadmaps, and acts as a trusted technical advisor, with occasional travel to customer sites.

Databricks · HuggingFace · LangChain +7

• London

Owns the product capability for post-training LLMs, turning research methods into deployable model improvements for customers. Builds reliable training harnesses, synthetic-data pipelines and evaluation workflows, applying SFT, preference optimisation, RLHF/RLAIF and verifier-based approaches. Works closely with Research and platform teams, with a roadmap spanning SaaS, VPC and air-gapped deployments.

PyTorch · LLMs · SaaS +1

• London

Embed with strategic enterprise customers to design, build and productionise custom fullstack data and AI applications on the Databricks platform. You own architecture and end-to-end delivery across data engineering, ML/AI integration and user-facing interfaces, partnering with Sales, Product and Field Engineering. Travel to client sites up to 50%.

Databricks · OpenAI · Anthropic +7

• London
• Hybrid
• £225,000 – £255,000 pa
• Mid-level

Join the Beneficial Deployments Life Sciences team to help labs automate experiments end-to-end with Claude, connecting software to robots, instruments and LIMS. You'll prototype and productionise LLM-powered tools, build evals for physical-world safety, and scale learnings through cookbooks and the Model Hardware Standard. Expect significant travel and EMEA ambassadorship at conferences.

Claude · Model Hardware Standard · Liquid Handlers +2

• London
• Hybrid · 1 day in office
• £165,000 – £190,000 pa
• Senior

Pre-sales architect acting as a trusted technical advisor to large enterprises, helping them integrate and deploy Claude across their technology stacks. The role spans technical discovery, evaluation frameworks, and scalable architecture design, working closely with Sales, Product, and Engineering teams and occasionally travelling to customer sites.

Claude · Claude API · Claude for Work

• London
• Hybrid
• Lead · manages people
• Permanent

Lead and grow a London-based engineering team building real-time safety and oversight systems for AI agents, including the Auto-review platform that evaluates agent actions at permission boundaries. The role is a hands-on builder-manager position, setting technical direction while shaping architecture, reviewing designs, debugging, and writing code, working closely with alignment researchers and product teams.

OpenAI

• London
• Hybrid · 3 days in office
• Mid-level
• Permanent

Partner directly with Digital Native customers to design, build and deploy AI-powered products, taking ideas from early experimentation through evaluation, production launch and scale. You will write and debug code, build evaluation systems, resolve complex integrations, and guide decisions on model behavior, reliability, latency, cost, safety and governance.

OpenAI

• London
• Hybrid
• Lead
• Permanent

Lead an ambitious research agenda at the intersection of LLMs, VLMs, VLAs and embodied AI, exploring how world knowledge, memory and reasoning can drive better physical-world decisions. Stay hands-on developing architectures, training and post-training large-scale models, while mentoring researchers and shaping the team's technical direction across London and Sunnyvale.

PyTorch

• London
• Hybrid
• Senior
• Permanent

Post-train and scale GAIA-family large video diffusion world models to generate multimodal synthetic driving data, running generation pipelines across tens of thousands of segments and optimising GPU inference. Sits between ML research and engineering, landing synthetic data into the same training stack as real driving data and measuring its impact on driving-model performance.

GAIA · GPU · KV Cache

• London
• Hybrid
• Mid-level
• Permanent

Design, build and deploy secure, scalable cloud infrastructure for AI and ML workflows, working in a cross-functional team with data scientists and ML engineers. The role involves infrastructure-as-code with Terraform, containerised deployments on Docker and Kubernetes, CI/CD and GitOps, plus client-facing requirements gathering and technical scoping.

Azure · AWS · Terraform +4

• London
• Hybrid
• £65,000 – £145,000 pa

Engineers at all levels build and scale the shared tooling and infrastructure behind the institute's AI safety research, from Inspect plugins and large-scale evaluation systems to model hosting and secure proxy infrastructure. Work spans open-source collaboration, supporting research teams with custom setups, and hardening infrastructure for frontier model evaluations.

Inspect

• London
• Mid-level
• Permanent

Own the science strategy for AI-powered brand safety and content classification, building LLM-driven multimodal systems that make billions of millisecond-latency decisions across web, mobile app, CTV and audio inventory. The role blends frontier research in adversarial ML, synthetic content detection and semantic understanding with large-scale production engineering, translating prototypes into deployed models and publishing at peer-reviewed venues.

Amazon DSP