Senior Software Engineer, Cloud, Simulation

• Evolve a core component of Wayve's simulation platform used to develop and evaluate driving intelligence. • Shape and implement the technical roadmap in one of three key areas: robot emulator fidelity, visual fidelity, or efficient scaling. • Work with a simulation approach that combines classical simulation techniques with cutting-edge developments in machine learning to represent the real world in high fidelity at scale.

• Have experience with workflow orchestration systems such as Airflow, Dagster, or Flyte and/or developing data-intensive applications. • Have excellent development skills in Python. • Have deep knowledge of Kubernetes at the user level. • Have a good sense of systems and data-oriented software engineering design, understanding what makes code reusable and extensible. • Understand common software performance issues and design tradeoffs. • Have 5+ years of industry experience designing and programming software. • Have excellent communication and people engagement skills.

• Have experience in the field of autonomous vehicles or robotics. • Be proficient with other programming languages like Go or C++. • Have experience scaling simulations or data-intensive workloads. • Have experience with design, implementation, and optimisation of large-scale machine learning inference systems running in cloud GPU environments. • Have experience operating and scaling modern machine-learned graphics techniques such as NeRF, Gaussian Splatting, or GenAI.

• Own key performance indicators for simulator cost, SLOs, throughput, latency, and more. • Work cross-company on aligning technical dependencies for simulator implementation. • Lead technical discussions and guide technical direction. • Effectively integrate the components of the simulated robot into the simulation platform. • Effectively integrate machine-learned graphics subsystems into the simulation platform. • Implement production-quality software in Python.

Wayve is one of the most exciting AI companies in the UK right now. They are tackling one of the hardest problems in technology, teaching machines to drive, and they are doing it with an approach that the rest of the industry is now converging towards. Backed by SoftBank, Microsoft, NVIDIA, Uber, Mercedes-Benz, Nissan, and Stellantis, Wayve has raised $2.8 billion in total funding and reached a valuation of $8.6 billion. With over 1,000 employees across London, Silicon Valley, Vancouver, Leonberg, Herzliya, and Tokyo, Wayve is scaling fast while keeping its London HQ at the centre. What stands out on Glassdoor (4.4/5 from 112+ reviews) is how consistently employees praise the culture, the calibre of colleagues, and the quality of the technical work. People describe it as some of the most interesting work of their careers. Wayve also offers an on-site chef, private healthcare, competitive pay with equity, and a genuine learning environment where you work alongside world-class ML researchers and engineers. That said, some reviews flag that the pace can be intense and that working across global time zones can stretch working hours. If you are an engineer, researcher, or operator who wants to work on genuinely frontier technology with real-world impact, and you thrive in fast-paced, mission-driven environments, Wayve is a rare opportunity.

• Work in a hybrid model from Wayve's London office with core working hours so you can determine the schedule that works best for you and your team. • Join a well-funded company backed by leading investors, founded in 2017 and pushing the boundaries of embodied AI. • Work in an inclusive environment that values diversity and embraces new perspectives.

Founded in 2017, Wayve is the leading developer of Embodied AI technology. Its advanced AI software and foundation models enable vehicles to perceive, understand, and navigate any complex environment, enhancing the usability and safety of automated driving systems. Wayve's approach to autonomous driving requires a simulator that is both highly realistic and highly descriptive, combining classical simulation techniques with cutting-edge machine learning.