Staff ML Performance Engineer, Performance Architecture

Wayve • Sunnyvale, CA

23h • Hybrid

About The Position

The Staff ML Performance Engineer will join Wayve’s Performance Architecture team within the AI Performance org. The team makes Wayve’s AI workloads faster and more efficient across training and cloud inference, enabling larger models to be trained faster and inference to run more efficiently at scale.

The role identifies, quantifies, and delivers optimisations across training and cloud inference workloads. It involves profiling workloads to uncover bottlenecks, implementing reusable improvements that work across targets, and tracking results through clear benchmarks. The engineer partners with Research and model teams to embed performance engineering in their development cycles, and with platform teams on cloud GPU hardware strategy and readiness.

Wayve operates a hybrid working model across its hubs in London, Sunnyvale, Yokohama, Herzliya, Vancouver, and Leonberg. The role may include hands-on work in vehicle workshops and labs. Location-dependent offerings include market-benchmarked salaries, equity, relocation and visa support where applicable, learning and development budgets, and comprehensive benefits.

Requirements

• 10+ years of industry experience driving performance engineering across ML systems, GPU compute infrastructure, distributed platforms, or similar.
• Experience optimising large-scale workloads on GPU compute clusters for training, inference, or both.
• Experience writing, reporting, and tracking performance benchmarks in an open and accessible way.
• Ability to write high-quality, well-structured, and tested Python code.
• BS or MS in Machine Learning, Computer Science, Engineering, or a related technical discipline, or equivalent experience.

Nice To Haves

• Experience with concurrent, parallel, and distributed computing.
• Experience optimising inference serving systems, including latency, throughput, batching, or caching.
• Experience using NVIDIA Nsight Systems or other system profilers.
• Experience implementing GPU kernels, such as with CUDA or Triton.
• Knowledge of computing fundamentals, including what makes code fast, secure, and reliable.

Responsibilities

• Profile machine-learning workloads across training and cloud inference to identify bottlenecks, using system- and kernel-level profilers.
• Design and implement efficiency improvements to maximise MFU, throughput, and utilisation, including approaches such as parallelism, compilation, mixed precision, and caching.
• Build reusable, cross-target optimisations, including kernels, data loaders, and frameworks such as Triton, rather than one-off workflow-specific fixes.
• Design and implement benchmarking tools to track efficiency gains and catch regressions on priority training and cloud inference workloads.
• Inform cloud GPU hardware strategy and readiness in partnership with platform teams.
• Build a culture of performance optimisation with Research and model teams.

Benefits

• Meaningful equity, sharing in the ownership and long-term success of Wayve.
• Relocation support and visa sponsorship where applicable.
• Hybrid working and core hours.
• 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, and team socials.
• Salaries benchmarked against the market annually.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior