Robotics Jobs in Woking

Wayve · London

Owns the end-to-end release cycle for AI driving models, coordinating ML engineering, evaluation, data science, platform, testing and product teams. Defines release gates and promotion criteria from training through validation to fleet deployment, drives regression investigations and rollback decisions, and builds tooling and KPIs to remove bottlenecks in a non-deterministic ML environment.

Wayve · London

Lead the technical direction for an offline 3D reconstruction system that turns raw vehicle sensor data into quality-assessed 3D ground-truth geometry and ego motion. The role spans SLAM, lidar point-cloud registration, state estimation and sensor fusion, with responsibility for architecture, roadmaps, prototypes and cross-functional alignment across perception, calibration and validation teams.

Humanoid · London

Own the hardware test methodology for a humanoid robot platform, defining component, subsystem and whole-robot tests from bench actuators to full walking integration sequences. Hands-on role: bring up prototypes, root-cause failures, and specify fixtures, jigs and production test requirements with Manufacturing Engineering and Quality. Sets pass/fail criteria, tolerances and traceable test data as the programme moves from EVT to production.

Humanoid · London

Own the hardware test methodology for a humanoid robot platform, defining component, subsystem and whole-robot tests and running them hands-on on early prototypes. Partner with automation, manufacturing and quality teams to translate methods into production test rigs, in-process checks and traceable acceptance criteria from EVT toward production.

Wayve · London

Lead functional safety activities for an end-to-end AI automated driving system, building Item Definitions, running HARA/FMEA/FTA analyses, and shaping safety concepts and architectures across vehicle platforms and third-party systems. Define verification and validation, including vehicle-level testing, and construct structured safety cases with claims, arguments and evidence for certification.

Wayve · London

Owns the technical vision and architecture for a platform that lets model engineers deploy, run, evaluate and improve ML models over petabytes of driving data on large GPU fleets. Combines hands-on backend engineering in Python with cross-team technical leadership, mentoring senior engineers and leading a small team of 2-3.

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 the L4 safety case.

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 for safety and performance, and builds CI/CD tooling and observability to catch issues earlier. Works across ML engineering, AI Platform, evaluation and product teams, balancing delivery speed with release rigour.

Wayve · London

Owns shared onboard runtime foundations for autonomous vehicles: application frameworks, communication middleware, lifecycle management, health monitoring and observability. Day to day involves C++ development, end-to-end latency and performance profiling across OS scheduling, memory hierarchy and thread synchronisation, plus porting the stack to new operating systems and compute platforms.

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

Lead a team of ML engineers and applied scientists building a production synthetic-data engine from generative world models like GAIA-3, covering rig and pose transfer, controllability and large-scale GPU inference. Own the loop from checkpoint and config through generation, evaluation and training, while driving inference optimisation, valid-generation yield and cross-team integration with driving-model and safety teams.