Staff ML Performance Engineer, Performance Architecture

Highlights

• Make AI workloads faster to unlock new autonomous driving capability.
• Profile training and cloud inference to find and deliver optimizations.
• Build benchmarking tools to track gains and catch regressions.

What they offer

• Meaningful equity
• Relocation support
• Hybrid working with core hours
• Learning and development budgets
• Health insurance, dental and wellbeing support
• Enhanced maternity and paternity leave
• Retirement or pension where applicable

Ideal candidate

A seasoned performance-minded engineer with 10+ years optimizing large-scale ML systems who loves digging into bottlenecks, sharing benchmarks openly, and embedding performance into research culture.

Requirements

• 10+ years of industry experience driving performance engineering across ML systems, GPU compute infrastructure, distributed platforms or similar
• Optimised large-scale workloads on GPU compute clusters, for training, inference or both
• Written, reported and tracked performance benchmarks in an open and accessible way
• Writes high quality, well-structured and tested Python code
• BS or MS in Machine Learning, Computer Science, Engineering or related technical discipline, or equivalent experience