Machine Learning Jobs

• London
• Hybrid · 1 day in office
• £260,000 – £630,000 pa
• Mid-level

Blends research and engineering to advance reinforcement learning for large language models, building agentic capabilities like computer use and autonomous code generation. Involves architecting distributed RL training infrastructure across GPU clusters, designing novel training environments and evaluations, and profiling and optimizing the stack. Works closely with alignment, red team, and production training groups, with a strong pair-programming culture.

PyTorch · TensorFlow · JAX +3

• London
• On-site
• £260,000 – £630,000 pa
• Mid-level

Own critical parts of a production pretraining pipeline, covering performance optimization, observability, reliability and hardware debugging across the full stack. The role splits roughly 50/50 between research and engineering, with on-call incident response during model launches and close collaboration with teams in SF and London.

JAX · TPU · PyTorch +3

• London
• Hybrid
• Lead
• Permanent

Lead the technical strategy, architecture and deployment of secure AI systems across high-stakes domains, bridging research and production. You'll set engineering standards for safe ML deployment, build scalable cloud infrastructure and multi-agent tooling, and partner with Frontier Labs on safety evaluations. The role also involves advising senior client stakeholders and mentoring engineering talent.

AWS · Azure · GCP +4

• London
• Hybrid
• Lead
• Permanent

Lead technical strategy, architecture and deployment of secure AI systems across high-stakes domains, bridging AI research and production. You'll build scalable ML infrastructure, champion safety-first frameworks and capability evaluations, advise senior client leadership, and mentor engineering talent while partnering with Frontier Labs on multi-agent tooling and AI governance.

AWS · Azure · GCP +4

• Cambridge
• On-site
• Mid-level
• Permanent

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.

Python · LLM

• London
• Mid-level
• Permanent

Builds and extends a data and ML platform that lets economists run analyses independently, automating pipelines and tooling with DBT, Python, Airflow, Spark, Iceberg and AWS services like Lambda, Bedrock and SageMaker. Works in a lean, no-on-call team without stand-ups or sprints, shaping technical direction for pricing, selection and delivery-speed projects.

DBT · Apache Airflow · Spark +6

• United Kingdom
• Entry level
• Internship

A 12-month paid placement in the AI & Algorithms department, working alongside engineers and mentors on client projects. Day-to-day work includes coding, modelling, training and testing models, data wrangling, and contributing to internal R&D on state-of-the-art AI. Interns join mixed-discipline teams, attend stand-ups and present results to colleagues and clients.

LLM · VLM · VLA

• London
• On-site
• £90,000 – £120,000 pa
• Senior
• Permanent

Design and build full-stack AI-powered product features spanning front-end, back-end, AI and infrastructure, including LLM, computer vision and agent-based systems. You will create evaluation, testing, prompt engineering and fine-tuning frameworks, working across DevOps and MLOps with Product and Engineering teams on financial crime compliance tooling.

TypeScript · React · Node.js +1

• United Kingdom
• Hybrid

Designs and builds proactive agentic systems, from sensor-edge reasoning to high-level planning, with stateful multi-turn reasoning, persistent memory and evaluation pipelines. Works across Python, TypeScript, LangGraph, CrewAI, MCP and cloud infrastructure, partnering with biologists and roboticists to take experimental AI from prototype to production.

NextJS · Pydantic · LangGraph +7

• London
• Hybrid
• Lead

Lead the applied AI transformation across a legal services group, identifying high-value opportunities and building LLM-powered solutions that reach production. Work with engineers, architects and senior stakeholders to deploy RAG and prompt-engineering solutions, define success metrics and monitor performance. Hybrid role with UK travel.

Azure · Databricks · Snowflake

• London
• Hybrid
• Permanent

Builds Python services and microservices that orchestrate the end-to-end model-release workflow, integrating feature candidates into release branches and automating readiness checks before training. Connects training platforms, simulation and on-road testing, surfacing quality gates and approvals, and productionises services on Azure and Kubernetes with strong observability. Works across Model Engineering, MLOps, Measurement, Evaluation, Simulation and Release Management.

Azure · Kubernetes · React +1

• London
• Hybrid
• Permanent

Design, build and operate shared infrastructure for language model access, agent runtimes and observability, taking projects from problem definition through technical design, deployment and ongoing operation. Partner with security, IT and engineering teams, and support adoption through troubleshooting, documentation and reusable patterns.

Kubernetes · MCP · RAG

• London
• Hybrid
• Permanent

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.

North

• London
• Remote
• Permanent

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.

JAX · PyTorch · XLA +3

• United Kingdom
• Remote
• Permanent

Builds agents that run real parts of the business, working in Marketing Engineering and forward-deployed to growth pods to find problems and own solutions end to end. Uses Python, SQL, LLM APIs and agent orchestration with frontier models, focusing on making agents reliable, measurable and safe enough for the business to depend on.

LLM APIs