Staff ML Performance Engineer (Inference Optimisation)

📋 Description

• Profile and pinpoint bottlenecks across the full inference stack (model graph, compiler/runtime
• Implement and validate optimisations in compilers, runtimes, and/or kernels (e.g. operator fusion
• Build robust benchmarking and regression testing to ensure performance improvements hold across
• Optimise for multiple targets (e.g. NVIDIA Orin/Thor, Qualcomm) and work with teams to support
• Collaborate with model developers to influence architecture and training/deployment decisions that
• Contribute to technical roadmaps and tooling and help raise the standard of performance engineering

🎯 Requirements

• Proven experience improving performance in production systems with tight constraints (latency
• Strong proficiency with at least one relevant stack/toolchain (e.g. TensorRT, CUDA, Qualcomm QNN
• Comfort operating at multiple levels of abstraction — from high-level model behaviour down to
• Strong software engineering fundamentals (debugging, profiling, testing, and maintainable code).
• Clear communicator and collaborative teammate; able to align multiple stakeholders on performance

🎁 Benefits

• Exposure to embedded or edge deployment of ML models, including benchmarking on real devices and
• 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.