Backed by SoftBank VF and Air Street.
About 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.
What they're looking for
- 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
More about this role
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!
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...
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