Staff ML Performance Engineer (Compiler)
Summary
Optimize transformer-based ML models for edge devices and GPUs to run Wayve’s autonomous driving software efficiently on in-vehicle hardware.
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
About you
Essential
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.
Desirable
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.
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