Staff ML Performance Engineer - Edge Inference Optimizer

Wayve; Greater London; On-site; GBP 120,000 - 180,000; Full time

Job summary

Wayve in London is seeking a Staff ML Performance Engineer to optimise ML inference for edge accelerators and GPUs, enabling our first driving product. This role blends ML systems, compilers, runtimes and embedded deployment across multiple early-stage projects.

You will set technical direction, partner with model developers and cross-functional teams to deliver performant, reliable edge AI capabilities on in-vehicle compute.

Qualifications

• Proven experience optimizing performance in production ML systems with tight latency/energy constraints.

Responsibilities

• Identify and implement optimisations in ML compilers, runtimes, and kernels to improve edge inference performance.
• Profile bottlenecks across the inference stack and deliver measurable improvements.
• Build robust benchmarking and regression testing for cross-model and cross-device validation.
• Develop and optimise for multiple target platforms (e.g. NVIDIA Orin/Thor, Qualcomm).
• Collaborate with model developers to influence architecture, training, and deployment for on-device efficiency.
• Contribute to roadmaps and tooling to raise performance engineering standards.

Skills

Performance optimization

Profiling & debugging

Cross-functional collaboration

Software fundamentals

Clear communication

Education

Bachelor's degree in CS or related field

Tools

CUDA

TensorRT

OpenCL

MLIR

ONNX

Job description

Wayve in London is seeking a Staff ML Performance Engineer to optimise ML inference for edge accelerators and GPUs, enabling our first driving product. This role blends ML systems, compilers, runtimes and embedded deployment across multiple early-stage projects.

You will set technical direction, partner with model developers and cross-functional teams to deliver performant, reliable edge AI capabilities on in-vehicle compute.