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Wayve is building an end-to-end AI platform for autonomous driving that enables vehicles to learn directly from real-world experience. The company is pioneering a mapless, hardware-agnostic approach that adapts and generalizes at scale.
You'll join the Core Simulation team, a mission-critical function within Wayve's driving intelligence development. Simulation enables rapid evaluation of changes, investigation of failures, and confidence-building before deployment to real vehicles. Wayve's distinctive challenge is that its end-to-end learned driving stack requires equally end-to-end simulation: reproducing behavior across sensor data, model inference, routing, control, and vehicle dynamics. The simulator must be realistic enough to provide meaningful signal, reproducible enough for teams to trust results, and descriptive enough to explain differences between simulated and on-road behavior.
You'll work hands-on in a shared, modern C++ and Python codebase. Your responsibilities include implementing scoped features, integrating driving-stack components and simulated robot systems, supporting different simulation configurations (initially high-scale open-loop evaluation), and investigating discrepancies between on-road and simulated execution. You'll improve automated testing, observability, reproducibility, and developer tooling; profile simulator workloads for performance and scalability gains; and collaborate across Simulation, Robotics, Autonomy, Evaluation, Platform, and Data teams.
Key focus areas: developing and maintaining production-quality simulation components, integrating new vehicle platforms and driving-stack elements, ensuring correct component interaction across workflows, root-cause analysis of simulation vs. reality gaps, and enhancing reproducibility, reliability, debuggability, and speed for larger evaluation workloads.
Wayve operates hybrid from main hubs in London, Sunnyvale, Yokohama, Herzliya, Vancouver, and Leonberg, combining in-person collaboration with remote flexibility.
REQUIREMENTS:
- Professional experience developing, delivering, and maintaining production software in collaborative engineering environments
- Strong software development skills in C++ and Python, with depth in at least one and effective working knowledge of the other
- Experience integrating and debugging components in complex software systems, reasoning across interfaces, data flows, timing, and failure modes
- Relevant experience in simulation (robotics or autonomous vehicles) at the integration level, or in another complex systems domain with transferable challenges
- Experience integrating and working with ML components (e.g., PyTorch models, CUDA, QNN)
- Strong software engineering fundamentals: testing, maintainability, code review, observability, pragmatic design
- Understanding of software performance issues and use of profiling/measurement to guide improvements
- Proficiency using GenAI development tools (coding assistants or agents) with careful review, testing, and validation
- Strong communication and collaboration skills; ability to deliver scoped work independently and raise blockers early
NICE TO HAVE:
- Experience with middleware or integrating components across complex software systems
- Experience with sensor data (camera, radar, lidar, GNSS) including modeling uncertainty or noise
- Performance profiling, observability, or debugging distributed systems
- Large-scale batch processing, cloud infrastructure, or GPU-based workloads
- Exposure to world models, NeRF, Gaussian splatting, or learned graphics
