Machine Learning Engineer (Compiler)

• Wayve is building autonomous driving technology that runs on real vehicles
• Getting our models onto embedded hardware — correctly, quickly, and reproducibly — is one of the hardest problems between research and product
• As a ML Compiler Engineer, you will own the compilation pipeline that makes that possible
• You will build and extend Wayve’s ML compiler end-to-end: designing passes, integrating with vendor toolchains like NVIDIA TensorRT and Qualcomm QNN, and delivering deployable bundles that meet our accuracy and latency requirements on every target platform
• Each stage in the pipeline — capture, decomposition, precision assignment, legalisation, partitioning — can affect accuracy, latency, or whether a vendor backend accepts the graph
• Your work spans the full lowering stack, building compiler passes and infrastructure that scale across architectures and target platforms
• Own the ML compilation pipeline end-to-end — from checkpoint to deployable bundle on NVIDIA (TensorRT) and Qualcomm (QNN) targets
• Design and implement compiler passes with accuracy and latency gates, so bad compiles are caught before they reach hardware
• Build compilation infrastructure that scales across platforms, model architectures, and SoCs — without re-engineering for each new target
• Partner with model and training teams on compilability; build regression and benchmarking to validate changes across releases
• Set technical direction and raise the bar for compiler engineering across the team
• Own the ML compilation pipeline end-to-end on NVIDIA (TensorRT) and Qualcomm (QNN) targets
• Design and implement compiler passes with accuracy and latency gates
• Extend precision typing and graph-splitting logic for new architectures and SoCs
• Partner with model and training teams on compilability
• Build regression and benchmarking to validate changes across releases
• Set technical direction and mentor on compiler design

Benefits

• Private healthcare: Choose our optional health insurance for comprehensive coverage for you and your family.
• Paid time off: Paid vacation plus public holidays and additional leave programs, ensuring you have time to unwind.
• Mental health resources: Through Spill, you can access therapy and mental health support.
• Community and socials: Join clubs or attend team socials to connect over hobbies, sports, or just for fun.
• Competitive compensation: Our compensation package includes cash and equity, making you a true partner in our success.
• Learning and development: Budgets for books, courses, and company-wide training to support your continuous growth.- 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