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About the Role
As a Staff ML Performance Engineer, you will optimize ML inference for edge accelerators and GPUs, focusing on running large transformer-based models efficiently on low-cost, low-power edge devices for Wayve's first driving product. You will help set the technical direction for production systems that run reliably on in-vehicle compute, working hands-on across ML systems, compilers, runtimes, kernels, and embedded deployment.
Responsibilities
- Identify, implement, and validate optimizations in ML compilers, runtimes, and kernels (e.g., operator fusion, scheduling, quantization-aware performance, custom kernels).
- Profile and pinpoint bottlenecks across the full inference stack (model graph, compiler/runtime, kernel execution, memory movement) and deliver measurable improvements.
- Build robust benchmarking and regression testing to ensure performance improvements hold across models, devices, and software releases.
- Develop and optimize for multiple target platforms (e.g., NVIDIA Orin/Thor, Qualcomm), working with cross-functional teams to deliver performant and maintainable solutions.
- Collaborate with model developers to influence architecture and training/deployment decisions that affect on-device performance.
- Contribute to technical roadmaps and tooling and help raise the standard of performance engineering across the team.
Requirements
- Proven experience improving performance in production systems with tight constraints (latency, memory, bandwidth, power/thermal, or cost).
- Strong proficiency with at least one relevant stack/toolchain (e.g., TensorRT, CUDA, Qualcomm QNN, Triton, OpenCL, MLIR, ONNX) and confidence learning adjacent frameworks quickly.
- Comfort operating at multiple levels of abstraction — from high-level model behavior down to low-level kernel/runtime execution.
- Strong software engineering fundamentals (debugging, profiling, testing, and maintainable code).
- Clear communicator and collaborative teammate; able to align multiple stakeholders on performance trade-offs and priorities.
- Experience with compute graph scheduling and execution on multiple targets.
- Exposure to embedded or edge deployment of ML models, including benchmarking on real devices and handling system-level constraints.
- Experience with NVIDIA and/or Qualcomm SoCs and performance tooling.
- Python and C++ proficiency.
- Experience mentoring others and/or driving technical direction in a small, fast-moving team.
Skills
- ML inference optimization
- Edge accelerators
- GPUs
- Transformer-based models
- ML systems
- Compilers
- Runtimes
- Kernels
- Embedded deployment
- Operator fusion
- Scheduling
- Quantization-aware performance
- Custom kernels
- Profiling
- Benchmarking
- Regression testing
- NVIDIA Orin/Thor
- Qualcomm
- TensorRT
- CUDA
- Qualcomm QNN
- Triton
- OpenCL
- MLIR
- ONNX
- Software engineering
- Debugging
- Testing
- Maintainable code
- Python
- C++
Experience Level
- Staff