Artificial Intelligence Jobs

Wayve · London

Owns the engineering release process for autonomous driving models, defining branching strategies, release cadence, configuration management and quality gates. Reviews model, architecture and code changes against intended outcomes, assesses evaluation results and builds CI/CD tooling and observability to deliver ML systems reliably at scale.

Wayve · London

Lead large-scale training and post-training for Gaia, Wayve's world model that predicts future driving video and runs closed-loop with the driving stack. Own model architecture decisions, push autoregressive rollouts toward longer, more stable generation, and make the model deployment-ready. Partner with research, applications, simulation engineering and cloud teams, mentoring and setting technical direction.

Faculty · London

Lead the design, build and deployment of production-grade machine learning systems for clients across pharma, healthcare and other sectors. Work spans architectural decisions, scalable infrastructure, MLOps standards and mentoring junior engineers, with a strong client-facing advisory element. Requires deep Python, TensorFlow or PyTorch, and cloud plus Docker/Kubernetes experience.

Faculty · Remote (UK)

Lead the design, build and deployment of production-grade ML systems for defence clients, covering architecture, scalable infrastructure and MLOps standards. Work cross-functionally with engineers, data scientists and commercial teams, acting as a technical advisor and mentoring junior engineers. Requires on-site work with customers 2-4 days per week and eligibility for UK Security Clearance.

Isomorphic Labs · Lausanne, Switzerland

Architect and secure an AI-first drug discovery platform, reporting to the CISO. You will build AI-specific risk frameworks, protect model artifacts, deploy LLM and agentic guardrails, and lead AI security incident response across distributed training and inference infrastructure.

Isomorphic Labs · London

Research engineers translate frontier research concepts into practical implementations, building and optimising state-of-the-art AI models such as Transformers, GNNs and diffusion models, plus the codebases, data pipelines and infrastructure for training and evaluation. They design and run experiments, analyse model performance, and advise on bringing models to production, working closely with research scientists in a cross-disciplinary team.

Harnham - Data and Analytics Recruitment · London · £75,000 – £85,000 pa

Owns the infrastructure, deployment and operational excellence of production machine learning systems within a growing AI and data organisation serving the public sector. Builds ML orchestration pipelines with Dagster, Airflow or Prefect, runs Kubernetes and Terraform/Bicep infrastructure, and maintains CI/CD and observability for models and LLM-powered solutions, working closely with data scientists and client-facing teams.

NVIDIA · Switzerland

Develops performance optimizations in deep learning frameworks using JAX, contributing to JAX core components and driving peak performance on NVIDIA hardware. Works with AI researchers to build future-proof models and tooling that bridges numerical computing, simulation and deep learning research. Requires C/C++ and Python skills plus deep knowledge of ML framework internals and CPU/GPU architectures.

NVIDIA · United Kingdom

Develops performance optimizations in deep learning frameworks using JAX, contributing to JAX core components and driving peak performance on NVIDIA hardware. Works with AI researchers to build future-proof models and tooling that bridges numerical computing, simulation and deep learning research. Requires C/C++ and Python, ML framework internals, and CPU/GPU architecture expertise.

Ncounter · Manchester · £80,000 – £90,000 pa

Hands-on research engineering role investigating the internal behaviour and security of large language models, turning academic ideas into practical experiments in Python. Work spans model internals, data movement, training risk and safe deployment, alongside production-quality ML engineering, testing and code reviews. Requires active SC clearance and around three days per week on secure sites.

Infused Solutions · London · £65,000 – £75,000 pa

Senior AI EngineerLocation: Remote (UK) or Hybrid - London (1 days per week in the office if London-based)Salary: £65,000 - £75,000 + Excellent BenefitsJob Type: PermanentPlease note: Applicants must have the right to work in the UK. Unfortunately, visa sponsorship...

Ocado · London

Owns the software engineering that brings data science and machine learning products into live production for the Autoplanner team, building AI-powered planning systems across global warehouses and last-mile assets. Writes typed, well-tested Python (with some Java for OSP integration), builds APIs and data contracts, manages AWS deployments and CI/CD, and packages, serves and monitors ML models. Acts as engineering partner to data scientists and shares on-call support.

Harnham - Data and Analytics Recruitment · London · £100,000 – £140,000 pa

Build and lead multimodal AI applications spanning text, audio and video, including AI-generated avatars and automated content summarisation, within a growing AI team in a mature data function. The role involves translating business needs into scalable, production-deployed AI products and collaborating with a multidisciplinary team, with technical leadership duties at Lead level.

Data Idols · London · £105,000 – £120,000 pa

Hands-on technical leadership role driving the design, development and deployment of AI, Machine Learning and Generative AI solutions, including LLMs and agentic AI. Partners with business and technology stakeholders to identify high-value use cases, prototype rapidly and take solutions through to production, while shaping the AI technical roadmap and engineering best practices.

Ocado · Sofia, Bulgaria

Maintains and improves a live customer-facing slot-availability application while writing Terraform/OpenTofu infrastructure as code for cloud environments. Extends backend services, APIs and data pipelines, and modernises the platform by tuning Airflow/Composer and reducing technical debt. Works in a small distributed team with monitoring, alerting and incident response duties.