ML Performance Engineer

Before the detail, here's the challenge you'd help us solve.

We build the embodied intelligence that moves real vehicles safely, and the ecosystem a billion machines will run on in the future. Very few people in AI can say this. Every role here, whatever the team, plugs into that.

Here’s what this particular role covers.

The Performance Architecture team is part of Wayve's AI Performance org. We make Wayve's AI workloads faster and more efficient across training and cloud inference, so that performance unlocks new product capability. Our work lets Wayve train larger models faster and run inference more efficiently at scale.

You'll identify, quantify and deliver optimisations across training and cloud inference workloads. You'll profile workloads to find bottlenecks, build optimisations that work across targets rather than one-off fixes, and track the gains with clear benchmarks. You'll work closely with Research and model teams to make performance engineering part of their development cycle, and with platform teams on cloud GPU hardware strategy.

• Profiling ML workloads across training and cloud inference to identify bottlenecks, using system and kernel level profilers

• Designing and implementing efficiency improvements to maximise MFU, throughput and utilisation, e.g. parallelism, compilation, mixed precision, caching

• Building reusable, cross-target optimisations (kernels, data loaders, frameworks such as Triton) rather than one-off, per-workflow fixes

• Designing and implementing benchmarking tools to track efficiency gains and catch regressions on priority training and cloud inference workloads

• Informing cloud GPU hardware strategy and readiness in partnership with platform teams

• Building a culture of performance optimisation with Research and model teams

• You have 10+ years of industry experience driving performance engineering across ML systems, GPU compute infrastructure, distributed platforms or similar

• You have optimised large-scale workloads on GPU compute clusters, for training, inference or both

• You have written, reported and tracked performance benchmarks in an open and accessible way

• You write high quality, well-structured and tested Python code

• You have a BS or MS in Machine Learning, Computer Science, Engineering or a related technical discipline, or equivalent experience

Nice to have:

• Experience with concurrent, parallel and distributed computing

• Experience optimising inference serving systems (e.g. latency, throughput, batching, caching)

• Experience using NVIDIA Nsight Systems or other system profilers

• Experience implementing GPU kernels (CUDA, Triton, etc.)

• Knowledge of computing fundamentals - what makes code fast, secure and reliable

Not ticking every box? That’s totally okay! If you’re passionate about autonomy and keen to learn, we encourage you to apply even if you don’t meet every requirement.

Wayve is building the leading AI platform for autonomous driving. We are pioneering an end to end AI approach that enables vehicles to learn directly from real world experience, developing the ability to adapt, generalise and improve tead of relying on hand coded rules or pre mapped environments, our AI Driver learns to drive by understanding the world around it. The result is technology that navigates complex urban environments with intelligence, precision and natural flow, unlocking meaningful advances in both safety and efficiency.

We believe autonomy represents a once in a generation transformation in how people and goods move, comparable to the shift from horses to cars, and from human driven vehicles to intelligent machines.

Our ambition is to make autonomy universal. Wayve’s mapless and…