Staff ML Performance Engineer (Training Efficiency)

Before the detail, here's the challenge you'd help us solve.

We build the embodied intelligence that moves real vehicles safely, and the ecosystem a billion machines will run on in the future. Very few people in AI can say this. Every role here, whatever the team, plugs into that.

Here’s what this particular role covers.

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.

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

#LI-HH1

A quick, honest note before you apply.

Wayve is not a mature, fully-structured place with the playbook already written. Much of how we work is still being written, and if you join, you’ll help write it. That suits people who want real ownership more than people who need a settled structure from day one.

If that sounds like the kind of problem you want to spend your time on, we’d really like to hear from you.