Staff ML Performance Engineer, Performance Architecture
$370k - $395k • Sunnyvale, California USA • Full-time
Posted 28d ago
Remote Work Policy
On-site
Employment Type
Full-time
Categories
Machine Learning Engineer
About the job
Wayve is building the AI platform for autonomous driving, pioneering an end-to-end AI approach that enables vehicles to learn directly from real-world experience. This role is within the Performance Architecture team, which focuses on making Wayve's AI workloads faster and more efficient across training and cloud inference. The goal is to unlock new product capabilities by enabling the training of larger models faster and running inference more efficiently at scale. The team plays a crucial role in the company's mission to make autonomy universal and accelerate the transition to a safer, more intelligent future of mobility.
Responsibilities
- Identify, quantify, and deliver optimizations across training and cloud inference workloads.
- Profile workloads to find bottlenecks using system and kernel-level profilers.
- Build reusable, cross-target optimizations (e.g., kernels, data loaders, frameworks like Triton) rather than one-off fixes.
- Design and implement efficiency improvements to maximize MFU, throughput, and utilization (e.g., parallelism, compilation, mixed precision, caching).
- Design and implement benchmarking tools to track efficiency gains and catch regressions.
- Inform cloud GPU hardware strategy and readiness in partnership with platform teams.
- Build a culture of performance optimization with Research and model teams.
Requirements
- 10+ years of industry experience driving performance engineering across ML systems, GPU compute infrastructure, distributed platforms, or similar.
- Experience optimizing 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.
- 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.
- Experience with concurrent, parallel, and distributed computing (nice to have).
- Experience optimizing inference serving systems (e.g., latency, throughput, batching, caching) (nice to have).
- Experience using NVIDIA Nsight Systems or other system profilers (nice to have).
- Experience implementing GPU kernels (CUDA, Triton, etc.) (nice to have).
- Knowledge of computing fundamentals (nice to have).
Benefits
- Salaries benchmarked against the market annually
- Meaningful equity
- Relocation support
- Visa sponsorship
- Hybrid working
- Core hours
- Learning and development budgets
- Support for training and conferences
- Comprehensive benefits including health insurance, dental, enhanced maternity and paternity leave, retirement or pension, access to therapists, wellbeing partnerships, team socials