Staff ML Performance Engineer

Who you are

• Proven experience improving performance in production systems with tight constraints (latency, memory, bandwidth, power/thermal, or cost)
• Strong proficiency with at least one relevant stack/toolchain (e.g. TensorRT, CUDA, Qualcomm QNN, Triton, OpenCL, MLIR, ONNX) and confidence learning adjacent frameworks quickly
• Comfort operating at multiple levels of abstraction — from high-level model behaviour down to low-level kernel/runtime execution
• Strong software engineering fundamentals (debugging, profiling, testing, and maintainable code)
• Clear communicator and collaborative teammate; able to align multiple stakeholders on performance trade-offs and priorities
• Experience with compute graph scheduling and execution on multiple targets
• Exposure to embedded or edge deployment of ML models, including benchmarking on real devices and handling system-level constraints
• Experience with NVIDIA and/or Qualcomm SoCs and performance tooling
• Python and C++ proficiency
• Experience mentoring others and/or driving technical direction in a small, fast-moving team
• We understand that everyone has a unique set of skills and experiences and that not everyone will meet all of the requirements listed above. If you’re passionate about self-driving cars and think you have what it takes to make a positive impact on the world, we encourage you to apply

What the job involves

• 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
• 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

The application process

• Step 1: Upload CV
• We will use your CV to fill as much of the application form as possible for you

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.