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Wayve is building an AI platform for autonomous driving using end-to-end learning from real-world experience. The ML Performance team optimizes inference for edge accelerators and GPUs, enabling large transformer-based models to run efficiently on low-cost, low-power in-vehicle compute.
In this hands-on role, you will work across ML systems, compilers, runtimes, kernels, and embedded deployment. You'll identify performance bottlenecks, implement optimizations, and collaborate with model developers on performance trade-offs to ensure deployment-aware decisions are made early in the development cycle.
Key responsibilities include:
- Profiling inference performance across model graphs, compiler/runtime behavior, kernel execution, and memory movement
- Implementing and validating optimizations in compilers, runtimes, and/or kernels, including fusion, scheduling, quantization-aware performance, and custom kernels
- Building benchmarking and regression tests to track performance across models, devices, and software releases
- Optimizing for edge targets such as NVIDIA Orin/Thor and Qualcomm platforms
- Contributing to team tooling, documentation, and technical discussions around ML performance
Wayve operates a hybrid model with main hubs in London, Sunnyvale, Yokohama, Herzliya, Vancouver, and Leonberg. The company offers competitive salaries benchmarked against market rates, meaningful equity, relocation support and visa sponsorship where applicable, hybrid working with core hours, learning and development budgets, and comprehensive benefits including health insurance, dental, enhanced parental leave, retirement/pension, and wellness partnerships.
The interview process includes an initial recruiter screen, deep-dive technical interviews (3 hours total covering programming, system design, and domain-specific topics), and a final mission & values alignment interview.
REQUIREMENTS:
- Experience improving performance in production or production-adjacent systems with latency, memory, bandwidth, power, thermal, or cost constraints
- Strong hands-on experience with at least one relevant stack: TensorRT, CUDA, Qualcomm QNN, Triton, or OpenCL
- Comfortable working from high-level model behavior down to kernel/runtime-level execution
- Strong software engineering fundamentals including debugging, profiling, testing, and maintainable code
- Clear communication and ability to work across ML, systems, and deployment teams
NICE TO HAVE:
- Experience deploying or benchmarking ML models on embedded or edge devices
- Familiarity with NVIDIA and/or Qualcomm SoCs and performance tooling
- Python and C++ proficiency
- Experience supporting other engineers or contributing to technical direction within a small team
