机器学习工程师，性能工具

About us

Founded in 2017, Wayve is the leading developer of Embodied AI technology. Our advanced AI software and foundation models enable vehicles to perceive, understand, and navigate any complex environment, enhancing the usability and safety of automated driving systems.

Our vision is to create autonomy that propels the world forward. Our intelligent, mapless, and hardware-agnostic AI products are designed for automakers, accelerating the transition from assisted to automated driving.

In our fast-paced environment big problems ignite us—we embrace uncertainty, leaning into complex challenges to unlock groundbreaking solutions. We aim high and stay humble in our pursuit of excellence, constantly learning and evolving as we pave the way for a smarter, safer future.

At Wayve, your contributions matter. We value diversity, embrace new perspectives, and foster an inclusive work environment; we back each other to deliver impact.

Make Wayve the experience that defines your career!

The role

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.

Key responsibilities

• 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.

About you

• You have built or significantly extended ML compilation or graph-lowering pipelines.

• You understand multi-st

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.

Top hard requirements (skills/experience)

• Built or owned significant parts of an ML compilation or graph-lowering pipeline.

• Deep experience with quantisation in compilation — precision typing, PTQ integration, debugging accuracy loss from compiler transforms.

• 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.

• Experience with multi-target compilation or graph partitioning across hardware backends.

• Ability to reason about correctness and performance trade-offs at each compiler stage.

Wayve is committed to creating an inclusive interview experience. If you require any accommodations or adjustments to participate fully in our interview process, please let us know.

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.

At Wayve we're committed to creating a diverse, fair and respectful culture that is inclusive of everyone based on their unique skills and perspectives, and regardless of sex, race, religion or belief, ethnic or national origin, disability, age, citizenship, marital, domestic or civil partnership status, sexual orientation, gender identity, veteran status, pregnancy or related condition (including breastfeeding) or any other basis as protected by applicable law.

For more information visit Careers at Wayve.

To learn more about what drives us, visit Values at Wayve

For US candidates only, please visit E-Verify Notice and Participation and Right to Work

DISCLAIMER: We will not ask about marriage or pregnancy, care responsibilities or disabilities in any of our job adverts or interviews. However, we do look to capture information about care responsibilities, and disabilities among other diversity information as part of an optional DEI Monitoring form to help us identify areas of improvement in our hiring process and ensure that the process is inclusive and non-discriminatory.

• You have built or significantly extended ML compilation or graph-lowering pipelines.

• You understand multi-stage lowering (capture, decomposition, precision assignment, legalisation) and can debug what breaks at each stage.

• Strong proficiency with at least one relevant stack (e.g. MLIR, ONNX, TensorRT, Qualcomm QNN, PyTorch export/capture) and confidence learning adjacent frameworks quickly.

• Experience with quantisation in compilation — precision typing, PTQ integration, and tracking down accuracy loss from compiler transforms.

• Comfortable from high-level model graphs down to vendor backend constraints; strong Python, with C++ a plus.

• Clear communicator who can align cross-functional teams on compilation trade-offs.

• 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.

• Vehicle impact — compiler passes determine what runs on embedded hardware in Wayve's driving product.

• Greenfield at Staff level — small team, high leverage, shaping the compilation stack from early stages.

• Scalable infrastructure — building pipelines that work across platforms and architectures without starting from scratch each time.