Staff ML Performance Engineer (Training Efficiency)

About the role

We are looking for a Staff ML Performance Engineer to join our Training Tech team working on optimizing large scale ML jobs to enable scaling our models to the next order of magnitude. A successful candidate will increase efficiency of training and inference workloads in order to allow Wayve to train larger models faster.

Key responsibilities

• Profile ML workloads to identify their bottlenecks, e.g. using NVIDIA Nsight Systems
• Design and implement efficiency improvements to maximize MFU and throughput, e.g. parallelism, model compilation, mixed precision
• Design and implement observability tools to identify bottlenecks and drive performance improvements, e.g. to track MFU, throughput, latency, etc
• Design and implement benchmarking tools, e.g. to track efficiency gains or regressions
• Collaborate closely with Research teams to integrate training efficiency improvements and create a culture of performance optimization

About you

• Essential: 10+ years of industry experience driving performance engineering across ML systems, GPU compute infrastructure, distributed platforms or similar field.
• Experience optimizing large scale jobs on GPU compute clusters.
• Experience in working in platform teams and working with research teams.
• Experience in 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

Desirable

• Experience working with concurrent, parallel and distributed computing.
• Experience using NVIDIA NSight Systems or other system profilers.
• Experience implementing GPU kernels (CUDA, Triton, etc).
• Knowledge of computing fundamentals - what makes code fast, secure and reliable.

Pay

The reasonably estimated salary for this role ranges from $336,400 to $359,000, plus a competitive equity package. Actual compensation is based on the candidate's skills, qualifications, and experience.

Schedule

This role is a full-time role based in Sunnyvale, CA (hybrid).