Machine Learning Jobs

• London
• Hybrid
• Lead · manages people
• Permanent

Lead a team of ML engineers and applied scientists building synthetic data from generative world models like GAIA-3, turning them into a production engine for training-grade driving data. Own the pipeline end to end, from checkpoint and large-scale GPU inference to artefacts landing in driving-model training, while setting technical direction on rig transfer, pose transfer and controllability.

Python · PyTorch · GPU +1

• New York, United States
• Hybrid · 2–3 days in office
• US$120,000 – US$240,000 pa

Builds and deploys deep learning and probabilistic models to predict and control physical systems, working alongside simulation engineers and customers. Owns data pipelines and production-ready models, and travels to customer sites roughly 3-4 weeks per quarter to co-develop solutions on-site.

NumPy · SciPy · Pandas +2

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

Join the Compute Infrastructure team to keep a planet-scale GPU accelerator fleet healthy and efficient. You'll build observability and telemetry pipelines, automate recovery, hunt compute waste, and optimise accelerator utilisation for large-scale training and inference, working closely with ML infrastructure and research teams.

GCP · Kubernetes · Nvidia GPU +1

• London
• Hybrid · 3 days in office
• Lead

Technically lead the Platform group, designing, operating and refining research, development and production cloud infrastructure across GCP, Kubernetes, Docker and Terraform. The role spans core technical decisions, tooling and CI/CD processes, working closely with scientists, ML researchers and product teams, and reports directly to the Director of Engineering.

GCP · Kubernetes · Docker +7

• Singapore
• Lead

Lead the deployment of ML models and engineering surrogates (deep learning on CAE/CFD/FEA data, time-series forecasting, anomaly detection, optimisation) into customer production environments. Own scoping, architecture and success metrics for data/ML systems, mentor engineers, and work on-site with customers across North America, Europe, Asia and Oceania for 3-4 weeks per quarter.

Python · PyTorch · Pandas +3

• 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

• London
• Hybrid
• Lead · manages people
• Permanent

Lead the scientific direction and architectural rigour of upstream AI safety and evaluation standards, partnering directly with Frontier Labs. Design novel experiment frameworks, mentor technical teams, and bridge deep research with client strategy across high-stakes engagements.

NumPy · Pandas · Inspect AI +1

• London
• Hybrid
• Mid-level · manages people
• Permanent

Lead a London-based team building the on-vehicle experiment platform that lets researchers deploy driving models to vehicles, run experiments and collect training data. You own people, roadmap and OEM partner relationships while staying hands-on with embedded Linux, C++/Python and over-the-air deployment across vehicle fleets.

NVIDIA Jetson · NVIDIA DRIVE · QNX +4

• London
• Lead
• Permanent

Owns delivery of priority AI initiatives for OpenAI's largest customers, leading complex deployments from scoping and planning through launch and adoption. Builds integrated delivery plans, resolves blockers, drives launch readiness and tracks adoption and consumption against agreed outcomes, working closely with account teams, FDEs and partners.

• London
• Remote
• £200,000 – £300,000 pa
• Lead

Own machine learning projects end to end, from experimentation through to production, building reliable, scalable systems with Python and large language models. Provide technical leadership to ML engineers, set engineering standards, lead code reviews and make architecture decisions, working closely with research and application engineering teams to improve model performance in production.

LLMs

• London
• Hybrid
• Senior
• Permanent

Architect novel generative models to design functional proteins, working in an interdisciplinary team of ML researchers, protein engineers and biologists. The role spans data curation, evaluation metrics, rapid prototyping, cloud training and inference optimisation, plus planning wet lab testing campaigns with the bio team.

• London
• Hybrid · 1 day in office
• £225,000 – £255,000 pa
• Mid-level

Partner-facing role embedding with nonprofits, research institutes and academic medical centres to apply Claude to clinical development for rare and neglected diseases. You'll design and review trial protocols, build tools for drug repurposing from literature, omics and patient data, and create reusable infrastructure like MCP servers and benchmarks.

Claude · MCP

• London
• Hybrid
• Mid-level · manages people
• Permanent

Lead and scale a team of Applied AI Engineers supporting early-stage and high-growth startup customers from experimentation to production. You'll coach engineers on architecture, evaluations, cost optimisation and migrations, partner with Sales on competitive pursuits, and turn recurring needs into repeatable playbooks and reference architectures.

OpenAI

• London
• Hybrid
• Senior

Builds AI-powered planning systems for global warehouses and last-mile operations, developing machine learning models, optimisation algorithms and operational simulations. Takes high-impact models from prototype to monitored production, sets technical direction and mentors data scientists while working with engineers, product managers and retail partners.

BigQuery · OR-Tools · SimPy

• 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