Staff ML Performance Engineer (Training Efficiency)

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

In order to set you up for success in this role, we’re looking for the following skills and experience.

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