214 jobs
PhysicsX London
Owns deployment of ML models and engineering surrogates (deep learning on CAE/CFD/FEA data, forecasting, anomaly detection) into customer production environments, building scalable training and inference pipelines with Python, PyTorch, Kubeflow and fastAPI. Leads scoping and architecture, mentors engineers, and travels to customer sites roughly 3-4 weeks per quarter.
PhysicsX New York, United States
Builds and automates high-fidelity electromagnetic models across low- and high-frequency applications, from chip and package design to EMI/EMC, using tools like HFSS, Maxwell and CST. Works at the intersection of CAE and data science, generating simulation data to train ML surrogates, collaborating with customers and mentoring junior engineers.
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.
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.
Wayve London
Owns the technical architecture and roadmap for LiDAR and cross-sensor 3D sensing, developing calibration, registration, temporal alignment and motion compensation methods. Blends algorithm research with production deployment in C++/Python, providing technical leadership and mentorship across the 3D sensing stack.
Wayve London
Lead functional safety activities for an end-to-end autonomous 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 to support certification.
Wayve London
Build and operate a globally distributed Data Transfer Hub moving petabytes of video, LiDAR and sensor data per day between Wayve and partners such as Uber, Stellantis and Nissan. Write pipeline logic in Python, manage infrastructure with Terraform, and use Flyte, Kafka and Azure for orchestration, event-driven lifecycle tracking and storage. The small, high-autonomy team owns production reliability, observability and cost efficiency, with an out-of-hours on-call rotation once the service is established.
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.
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
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
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 and 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
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.
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
You'll design, build and operate shared infrastructure for model access, agent runtimes and observability, taking projects from problem definition through technical design, deployment and ongoing operation. Working with security, IT and engineering teams, you'll create governed access layers, reusable tooling and standards so teams can run AI agents in production without one-off infrastructure.
Wayve London
Develops and maintains Wayve's Core Simulation system in modern C++ and Python, integrating driving-stack components, simulated robot systems and vehicle-platform capabilities. Initially focused on high-scale open-loop evaluation, the role involves debugging on-road versus simulated behaviour, improving reproducibility, observability and automated testing, and profiling simulator performance across a shared codebase.
