The role

As a Staff ML Performance Engineer, you’ll play a key role in high-impact projects, optimising ML inference for edge accelerators and GPUs. The focus of this team is to run large transformer-based models efficiently on low-cost, low-power edge devices to enable Wayve’s first driving product.

You’ll help set the technical direction for turning these models into production systems that run reliably on in-vehicle compute. This is a hands-on role working across ML systems, compilers, runtimes, kernels, and embedded deployment, contributing to several early-stage, high-impact projects at Wayve.

Key responsibilities:

• Identify, implement, and validate optimisations in ML compilers, runtimes, and kernels (e.g. operator fusion, scheduling, quantisation-aware performance, custom kernels)
• Profile and pinpoint bottlenecks across the full inference stack (model graph, compiler/runtime, kernel execution, memory movement) and deliver measurable improvements.
• Build robust benchmarking and regression testing to ensure performance improvements hold across models, devices, and software releases.
• Develop and optimise for multiple target platforms (e.g. NVIDIA Orin/Thor, Qualcomm), working with cross-functional teams to deliver performant and maintainable solutions.
• Collaborate with model developers to influence architecture and training/deployment decisions that affect on-device performance.
• Contribute to technical roadmaps and tooling and help raise the standard of performance engineering across the team.