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
As a Staff ML Performance Engineer, you’ll play a key role in high-impact projects, optimising ML inference for edge accelerators and GPUs. The focus of this team is to run large transformer-based models efficiently on low-cost, low-power edge devices to enable Wayve’s first driving product.
You’ll help set the technical direction for turning these models into production systems that run reliably on in-vehicle compute. This is a hands-on role working across ML systems, compilers, runtimes, kernels, and embedded deployment, contributing to several early-stage, high-impact projects at Wayve.
Key responsibilities:
- Profile and pinpoint bottlenecks across the full inference stack (model graph, compiler/runtime, kernel execution, memory movement) and deliver measurable improvements.
- Implement and validate optimisations in compilers, runtimes, and/or kernels (e.g. operator fusion, scheduling, quantisation-aware performance, custom kernels).
- Build robust benchmarking and regression testing to ensure performance improvements hold across models, devices, and software releases.
- Optimise for multiple targets (e.g. NVIDIA Orin/Thor, Qualcomm) and work with teams to support these in a maintainable way
- 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
About you
Essential
- 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) and confidence learning adjacent frameworks quickly.
- Comfort operating at multiple levels of abstraction — from high-level model behaviour 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.
Desirable
- 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.