Artificial Intelligence Jobs in Leeds

Wayve · London

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 delivery, plus partner and OEM relationships, while staying close to embedded Linux systems code in C++ or Python.

Wayve · London

Lead a small team iterating on Wayve's end-to-end L2 driving model, balancing hands-on Python coding and experiments with directing priorities. You'll root-cause on-road issues and fix them through data recipes, model architecture, evaluation and release readiness, working across teams on model and vehicle interface problems.

Faculty · London

Deliver bespoke, production-grade machine learning systems for government and national security clients, spanning the full ML lifecycle from architecture to deployment. Work with Python, Scikit-learn, TensorFlow or PyTorch, plus cloud platforms and Docker/Kubernetes, while advising stakeholders and setting deployment standards.

Faculty · London

Builds and deploys production-grade machine learning systems for national security and AI safety clients, including LLM applications, multi-agent tooling and evaluation of frontier models. Involves technical scoping, architectural decisions, and advising stakeholders, with reusable infrastructure work alongside engineers and data scientists.

Synthesia · Seattle, United States · US$200,000 pa

Builds and operates core enterprise platform systems, ensuring security, reliability and scalability for large customers, and contributes to a new API suite exposing Avatar technology to external creative tools. Owns multi-month, cross-team projects, translating business and regulatory requirements into scalable designs, and works with Python, React and AWS alongside product, security, legal and infrastructure partners.

Ocado · Kraków, Poland

Senior ML engineer role in a cross-craft data science team building prediction, ranking and personalisation systems for an e-commerce platform. Owns the full ML lifecycle on production systems, from experimentation and training through serving, monitoring and MLOps, while coaching others and shaping reusable platform building blocks.

Wayve · London

Lead the Applied Methods Data Science team, partnering with the Autonomy organisation to turn experimental and observational analysis of real and simulated driving into evaluations and insights. Set three-, six- and 12-month roadmaps, review technical proposals for statistical rigour, hire and grow data scientists, and build tooling to scale the team's capability.

Faculty · London

Builds and deploys production-grade ML software, tools and infrastructure, creating reusable solutions that accelerate ML delivery and capability testing of frontier AI models. Leads technical scoping and architectural decisions, advises clients and partners, and works with frontier labs on AI safety red teaming and evaluation.

Wayve · London

Lead a newly dedicated Runtime Platform team building the application frameworks, middleware, IPC, performance and observability foundations that let Wayve's onboard AI Driver run in real time. The role is people-first, combining coaching, hiring and roadmap ownership with technical direction across latency, profiling, real-time scheduling and embedded Linux, partnering with vehicle software, hardware and OEM programme teams.

Faculty · London

Oversees a portfolio of Applied AI projects as the senior point of contact for clients and internal teams, managing delivery quality, commercial margins and scope. Combines strategic consulting with business development, writing proposals and growing accounts, while mentoring cross-functional teams using Agile, Scrum or Kanban. Based in the Professional and Financial Services unit serving highly regulated industries.

Faculty · Remote (UK)

Deliver bespoke, production-grade machine learning systems for defence clients, covering the full ML lifecycle from architecture and scoping to deployment. Work with Python, Scikit-learn, TensorFlow or PyTorch, plus cloud platforms and Docker/Kubernetes, splitting time between client sites and remote work.