Staff ML Performance Engineer: Edge Inference Optimizer

EngineersOfAI; Greater London; Hybrid; GBP 120,000 - 180,000; Full time

Job summary

Wayve in London is seeking a Staff ML Performance Engineer to optimise inference on edge accelerators and GPUs for our first driving product. You will shape the technical direction for production systems running on in-vehicle compute, spanning ML systems, compilers, runtimes and embedded deployment.

This role combines hands-on work with strategy across multiple early-stage projects. The ideal candidate will have deep experience optimizing production ML workloads under tight constraints and be

Qualifications

• Proven experience improving performance in production systems with tight constraints.
• Strong proficiency with ML stacks and inference toolchains; ability to learn adjacent frameworks quickly.
• Comfort operating across high-level models to low-level kernel/runtime execution.
• Solid software engineering fundamentals and debugging expertise.
• Excellent communication and collaboration across multiple stakeholders.

Responsibilities

• Identify, implement and validate optimisations in compilers, runtimes, and kernels.
• Profile bottlenecks across the full inference stack and deliver measurable improvements.
• Build benchmarking and regression tests to ensure performance across models and devices.
• Develop and optimise for multiple target platforms (e.g., NVIDIA Orin/Thor, Qualcomm).
• Collaborate with model developers to influence architecture and deployment decisions.
• Contribute to roadmaps and tooling to raise performance engineering standards.

Skills

Performance optimization

ML inference

Edge deployment

CUDA/TensorRT

C++/Python

Embedded systems

Tools

TensorRT

CUDA

Qualcomm QNN

Triton

OpenCL

MLIR

Job description

Wayve in London is seeking a Staff ML Performance Engineer to optimise inference on edge accelerators and GPUs for our first driving product. You will shape the technical direction for production systems running on in-vehicle compute, spanning ML systems, compilers, runtimes and embedded deployment.

This role combines hands-on work with strategy across multiple early-stage projects. The ideal candidate will have deep experience optimizing production ML workloads under tight constraints and be