Machine Learning Engineer (Compiler)

Join Wayve, a pioneering company in autonomous driving technology. As a Machine Learning Engineer (Compiler), you will be responsible for owning the entire ML compilation pipeline, from checkpoint to deployable bundle on NVIDIA and Qualcomm targets. You will design and implement compiler passes, build scalable compilation infrastructure, and partner with model and training teams. This is a unique opportunity to work on cutting-edge technology and make a significant impact in the field of autonomous driving.

• Ownership of the ML compilation pipeline end-to-end, from checkpoint to deployable bundle on NVIDIA (TensorRT) and Qualcomm (QNN) targets.
• Designing and implementing compiler passes with accuracy and latency gates, ensuring that bad compiles are caught before they reach hardware.
• Building compilation infrastructure that scales across platforms, model architectures, and SoCs, without re-engineering for each new target.

• Experience with quantisation in compilation — precision typing, PTQ integration, and tracking down accuracy loss from compiler transforms
• Vehicle impact — compiler passes determine what runs on embedded hardware in Wayve's driving product
• Real compiler ownership — full lowering pipeline from checkpoint to deployable bundle, working deeply with TensorRT and QNN
• Hard problems — quantisation preservation through decomposition, cross-SoC precision typing, graph partitioning under speed/accuracy trade-offs, legalisation that does not silently break earlier passes
• Strong proficiency with at least one relevant stack (e.g. MLIR, ONNX, TensorRT, Qualcomm QNN, PyTorch export/capture) and confidence learning adjacent frameworks quickly
• Comfortable from high-level model graphs down to vendor backend constraints; strong Python, with C++ a plus
• Scalable infrastructure — building pipelines that work across platforms and architectures without starting from scratch each time
• Clear communicator who can align cross-functional teams on compilation trade-offs
• You understand multi-stage lowering (capture, decomposition, precision assignment, legalisation) and can debug what breaks at each stage
• Greenfield at Staff level — small team, high leverage, shaping the compilation stack from early stages
• You have built or significantly extended ML compilation or graph-lowering pipelines
• Experience with multi-target compilation or graph partitioning across hardware backends
• Strong Python; comfortable building and testing compiler infrastructure in production codebases
• Proficiency with at least one of: MLIR, ONNX, TensorRT, Qualcomm QNN, PyTorch graph capture/export
• Deep experience with quantisation in compilation — precision typing, PTQ integration, debugging accuracy loss from compiler transforms
• Ability to reason about correctness and performance trade-offs at each compiler stage
• Built or owned significant parts of an ML compilation or graph-lowering pipeline
• 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