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

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

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.

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Staff ML Performance Engineer (Training Efficiency)