Staff ML Performance Engineer (Training Efficiency)

Wayve

$336K — $359K *
Information Technology
8 - 10 years of experience
Job Overview by Ladders

Qualifications

  • 10+ years of industry experience in performance engineering for ML systems, GPU compute infrastructure, or similar fields.
  • Experience optimizing large scale jobs on GPU clusters.
  • Proven track record of collaborating with platform and research teams.
  • Skilled in writing, reporting, and tracking performance benchmarks transparently.
  • Proficient in high-quality, structured Python coding.
  • BS or MS in Machine Learning, Computer Science, Engineering, or equivalent experience.

Responsibilities

  • Profile ML workloads to identify performance bottlenecks using profiling tools.
  • Design and implement efficiency improvements for maximum throughput and model performance.
  • Develop observability tools to monitor performance metrics like MFU and latency.
  • Create benchmarking tools to assess efficiency gains and regressions.
  • Collaborate with Research teams to roll out training efficiency improvements.

Benefits

  • Full-time position with a hybrid working model in Sunnyvale, CA.
  • Competitive equity package offered.
Full Job Description
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

Similar Jobs

More Jobs at Wayve

More Information Technology Jobs

Find similar Staff ML Performance Engineer (Training Efficiency) jobs: