Software Engineer (Simulation, Evaluation, Validation) in London

As a software engineer in Wayve’s Core Simulation team, you will help develop the simulation system used to evaluate and improve Wayve’s driving intelligence. Wayve’s end-to-end driving stack creates unique simulation challenges. Rather than testing independently specified modules, we need to reproduce the behaviour of a learned driving system across a complex combination of sensor data, model inference, routing, control and vehicle dynamics. Our simulator must be realistic enough to provide meaningful signal, reproducible enough for teams to trust the results, and descriptive enough to understand differences between simulated and on-road behaviour.

Our open-loop and closed-loop simulation workflows share a common codebase, with different components executed and configured depending on the use case. You will initially work with the team focused on high-scale open-loop evaluation, while contributing to shared components and infrastructure used across the broader simulation system. This is a hands-on software engineering role working across C++ and Python. You will take ownership of scoped features and integrations, working closely with engineers across Simulation, Robotics, Autonomy, Evaluation, Platform and Data to make simulation accurate, reliable, scalable and useful to its users.

• Develop and maintain production-quality components within Wayve’s simulation system using modern C++ and Python.
• Integrate driving-stack components, simulated robot systems and new vehicle-platform capabilities into the shared simulation environment.
• Support different simulation configurations and ensure components interact correctly across applicable workflows.
• Investigate differences between on-road and simulated execution, identifying issues across data, inference and simulated components.
• Improve simulation reproducibility, reliability and debuggability through automated testing, observability and better developer tooling.
• Profile and improve simulator performance, helping us run increasingly large evaluation workloads efficiently.
• Work with internal users and adjacent engineering teams to clarify requirements, resolve integration issues and deliver scoped improvements.
• Contribute to technical designs and help evolve the quality and maintainability of the simulation codebase.

Benefits:

• Private healthcare: Choose our optional health insurance for comprehensive coverage for you and your family.
• Paid time off: Paid vacation plus public holidays and additional leave programs, ensuring you have time to unwind.
• Mental health resources: Through Spill, you can access therapy and mental health support.
• Community and socials: Join clubs or attend team socials to connect over hobbies, sports, or just for fun.
• Competitive compensation: Our compensation package includes cash and equity, making you a true partner in our success.
• Learning and development: Budgets for books, courses, and company-wide training to support your continuous growth.

Experience:

• Using GenAI development tools, such as coding assistants or agents, to improve productivity while carefully reviewing, testing and validating their output.
• Good understanding of software and data-oriented design, including how to build code that is maintainable, reusable and extensible.
• Strong communication and collaboration skills.
• Strong software development skills in C++ and Python, with depth in at least one and the ability to work effectively in both.
• Experience debugging complex systems and reasoning about interfaces, data flows, timing and failure modes.
• Ability to independently deliver scoped features, communicate progress clearly and raise risks or blockers early.
• Relevant experience in simulation, robotics, autonomous systems or similarly complex systems involving multiple interacting software components.
• Understanding of common software performance issues and the ability to make pragmatic design trade-offs.
• Professional experience developing, delivering and maintaining production software in a collaborative engineering environment.
• Experience in autonomous vehicles or other robotics applications.
• Experience simulating or modelling vehicles, robots or their dynamics.
• Experience with middleware or integrating components across complex software systems.
• Experience working with sensor data such as camera, radar, lidar or GNSS, including modelling uncertainty or noise.
• Experience integrating machine-learning inference into production systems.
• Experience with performance profiling, observability or debugging distributed systems.
• Familiarity with large-scale batch processing, cloud infrastructure or GPU-based workloads.