📋 Description

• Design and build reliable, self-service performance tools that scale across models, hardware
• Shape how Wayve measures and predicts AI performance, and set the standards other teams build on.
• Model theoretical peak for a platform, compare with achieved performance, and pinpoint where
• Predict latency, memory, utilization, and compute cost of a model or recipe change before spending
• Own monitoring and regression alerting across model builds and training runs.
• Work with training and runtime engineers to set performance targets and make the case with data.

🎯 Requirements

• Deep, hands-on performance engineering in complex systems: profiling, roofline analysis, latency
• A track record of owning a tool or service end to end — design, delivery, and adoption by other
• Strong Python skills, and comfort profiling and instrumenting large production codebases.
• Hands-on experience developing deep learning models with PyTorch.
• Data analysis skills to turn noisy measurements into conclusions you can defend.
• Judgment to turn an ambiguous performance question into a measurable one, and to prioritize what