Artificial Intelligence Jobs

Faculty · London

Build and lead a Professional Services practice, shaping go-to-market strategy and leading complex AI transformation programmes for major firms. Act as a strategic advisor to C-suite leaders, driving commercial growth and guiding multidisciplinary teams of engineers and data scientists. Focus on agentic AI in regulated, knowledge-work-heavy sectors.

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.

Wayve · London

Sets technical strategy and roadmap for fault detection and fallback systems in an autonomous driving stack, designing and training detection mechanisms for robust driverless operation. Leads integration into the shared driving stack, aligning ML, inference optimisation and software teams, and mentors through architecture reviews. Works with large-scale training and fleet data in a small, high-impact team.

Wayve · London

Works at the boundary between onboard software and vehicle hardware, bringing up new vehicle platforms on benches, HiL systems and vehicles, including at international test locations. Builds and maintains vehicle-facing components and shared frameworks in C++, Python or Rust on Linux-based robotic and automotive systems using ROS2, CAN and Automotive Ethernet, with diagnostics, tooling and documentation for long-term supportability.

OpenAI · London

You'll partner with early-stage startups to embed Codex into their software workflows, building integrations, automations and reusable tools while troubleshooting technical blockers. A large part of the role is outward-facing: leading technical talks, live demos, workshops and hackathons, then feeding developer insights back to OpenAI's product and research teams.

OpenAI · London

Design and build distributed failure detection, tracing, and profiling systems for large-scale AI training workloads, identifying slow or faulty nodes and surfacing performance bottlenecks. Work deep in the stack across collective communication, parallelism, fault tolerance, and observability, collaborating with systems, infrastructure, and research teams to evolve the training platform.

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.

Faculty · Remote (UK)

Lead backend and edge/IoT engineering for high-stakes Defence projects, bridging proof-of-concept work and robust production systems. You will mentor a squad of engineers, design complex components, and build CI/CD and containerisation pipelines with Docker and Kubernetes while shipping Python and compiled-language code alongside ML engineers and data scientists.

Faculty · London

Lead and grow a banking practice within an applied AI consultancy, shaping go-to-market strategy and building relationships with C-suite banking leaders. You will drive complex AI transformation programmes from strategy through software delivery, generate revenue through pitches and demos, and mentor multidisciplinary teams. The role blends commercial ownership with deep sector expertise in a regulated industry.

Wayve · London

Leads complex, multi-team programmes across Level 2 and Level 4 autonomous driving products, translating product milestones into integrated cross-team schedules and delivery plans. Reviews technical progress, backlog health and system-level evidence to assess milestone readiness, managing dependencies and critical-path activities across engineering, product, safety and validation. Improves lifecycle-planning practices and tooling to surface risk earlier and deliver more predictably.

Wayve · London

Owns the autonomy performance measurement framework for a public-scale robotaxi deployment, defining metrics, dashboards and release gates for an end-to-end learned driving system with no existing playbook. Runs the performance improvement loop across model, robot software, backend and evaluation tooling, leading cross-functional execution without direct reporting authority while staying hands-on in Python, C++ and SQL.

Wayve · London

Builds reusable Python libraries and platform tooling for multimodal data loading, distributed training, inference, checkpointing and observability, supporting ML teams running models across thousands of GPUs. Partners closely with researchers to turn complex workflows into stable, scalable software, leading projects from discovery to delivery. A software and platform engineering role rather than ML modelling.

Wayve · London

Develop next-generation generative world models and planners such as GAIA, simulating complex driving environments for faster training and scalable deployment. Work spans diffusion and transformer architectures, latent compression, context pruning and inference-latency optimisation, with closed-loop agent interaction and sim-to-real evaluation. The role combines ML research, multi-modal modelling and production deployment, including mentoring and publishing.

Wayve · London

Owns the end-to-end architecture and technical roadmap for Remote Assistance, the real-time bridge between Wayve's AI Driver and human operators, spanning onboard vehicle integration, offboard services and operator HMI. Defines the vehicle-operator contract, fallback/MRM behaviour and latency budgets, and leads cross-org execution across autonomy, safety, platform and product teams to support L4 operation.

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.