Senior ML Performance Engineer, Inference Optimisation

The ML performance team optimises inference for edge accelerators and GPUs, so large transformer-based models run efficiently on low-cost, low-power in-vehicle compute. The team works closely with model, platform and deployment teams to turn research models into reliable production systems for Wayve's driving product. This is a hands-on role across ML systems, compilers, runtimes, kernels and embedded deployment. The role involves finding bottlenecks, implementing performance improvements, and working with model developers on performance trade-offs so deployment-aware decisions are made early. Interview process: initial call / recruiter screen; HM meeting (30 mins); deep-dive technical interviews (kernel optimisation, system design & domain-specific interviews; 3 hours total); final interview on mission & values alignment.

Responsibilities

Profiling inference performance across model graphs, compiler/runtime behaviour, kernel execution and memory movement; Implementing and validating optimisations in compilers, runtimes and/or kernels, including fusion, scheduling, quantisation-aware performance and custom kernels; Building benchmarking and regression tests to track performance across models, devices and software releases; Optimising for edge targets such as NVIDIA Orin/Thor and Qualcomm platforms; Contributing to team tooling, documentation and technical discussions around ML performance

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, such as TensorRT, CUDA, Qualcomm QNN, Triton or OpenCL; Comfortable working from high-level model behaviour down to kernel/runtime-level execution; Strong software engineering fundamentals, including debugging, profiling, testing and maintainable code; Communicates clearly and works well 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

Benefits

Salaries benchmarked against the market annually; Meaningful equity; Relocation support and visa sponsorship where applicable; Hybrid working, core hours and the chance to work hands on in vehicle workshops and labs; Learning and development budgets with support for training, conferences and growth; Comprehensive benefits including health insurance, dental, enhanced maternity and paternity leave, retirement or pension where applicable, access to therapists, wellbeing partnerships, team socials and more