Staff ML Performance Engineer, Performance Architecture

Overview

Join Wayve to optimize AI workloads for autonomous driving. You’ll drive performance improvements across ML training and cloud inference, enabling larger models and faster deployment. You’ll collaborate with Research and model teams to embed performance into development cycles and influence cloud GPU strategies. Your work will scale efficiency across targets with reusable optimisations and benchmarking. This role offers hands-on impact in a fast-moving, mission-driven environment.

Pay / Benefits

• hybrid working model
• learning and development budgets
• meaningful equity
• relocation support and visa sponsorship
• health insurance and wellbeing offerings
• enhanced maternity/paternity leave

Responsibilities

• Profile and optimize ML workloads for training and cloud inference to reduce bottlenecks
• Design and implement cross-target optimisations (kernels, data loaders, frameworks like Triton)
• Develop benchmarking tools to quantify efficiency gains and detect regressions
• Collaborate with Research, model teams, and platform teams to align performance with product goals
• Inform cloud GPU hardware strategy and readiness
• Foster a culture of performance optimization across teams

Key requirements

• 10+ years in performance engineering for ML systems or GPU compute infrastructure
• Experience with large-scale GPU workloads (training or inference)
• Proven ability to write, report and track performance benchmarks
• High-quality, well-structured Python code
• BS or MS in Machine Learning, Computer Science, Engineering or related field, or equivalent experience

• cross-functional collaboration
• strong communication of benchmarks and results
• context-driven problem solving
• GPU compute infrastructure
• profiling tools and kernel-level profiling
• CUDA or Triton GPU kernel development (nice to have)

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