Robotics Jobs in Woking

Humanoid · London

Lead the design of state estimation, long-term mapping and localisation loops, integrating modern machine learning and foundation geometry models into the core navigation stack. Train and deploy large-scale multimodal networks for ultra-low latency performance on edge hardware, building 3D spatial representations that give robots persistent spatial memory. Works cross-functionally between research and reliable robot integration.

Wayve · London

Post-train and scale GAIA-family large video diffusion world models to generate multimodal synthetic driving data, running generation pipelines across tens of thousands of segments and optimising GPU inference. Sits between ML research and engineering, landing synthetic data into the same training stack as real driving data and measuring its impact on driving-model performance.

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.

Humanoid · London

Lead a team of technicians building, reworking and repairing complex humanoid robots, balancing build quality with high-velocity delivery. The role is hands-on, covering electro-mechanical assembly, harness routing, sensor installation and functional testing, while reporting risks and blockers to senior leadership and driving team culture and continuous improvement.

Wayve · London

Develops next-generation generative world models and planners such as GAIA, using diffusion, transformer and hybrid architectures to simulate complex driving environments for training, testing and deployment. The role spans ML research, multi-modal modelling and real-world deployment, with a focus on inference efficiency, closed-loop agent interaction and sim-to-real evaluation.

Wayve · London

Owns validation strategy for ADAS and L2 autonomy functions, translating product, safety and regulatory requirements into test scenarios, metrics, coverage and acceptance criteria. Works hands-on with Python evaluation pipelines, simulation and SQL data analysis, and can be hired as a senior individual contributor or as a manager leading validation delivery.

Wayve · London

Owns the technical architecture and roadmap for LiDAR and cross-sensor 3D sensing, leading development of target-based and targetless calibration, LiDAR-to-camera registration, temporal alignment and motion compensation using LiDAR odometry, nonlinear least-squares optimisation and factor graphs. A senior individual-contributor role combining algorithm development, evaluation methodology and productionisation, with technical leadership and mentorship across the 3D sensing stack.

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.

Humanoid · London

Senior systems engineering role focused on the software and autonomy stack of a humanoid robot platform, spanning perception, localisation, navigation, AI, motion planning, controls and ROS 2. Involves translating product needs into requirements, architectures and interfaces, coordinating integration across teams, and defining verification and validation strategies for autonomous behaviours.