Staff Ml Performance Engineer (training Efficiency)

Wayve

Sunnyvale, United States
On-site
Ml workload profiling
Efficiency improvements
Observability tools
Wayve is seeking a Staff ML Performance Engineer to optimize large-scale machine learning jobs, enhancing the efficiency of training and inference workloads in the context of autonomous driving technology. The ideal candidate should have extensive experience in performance engineering, particularly in GPU compute infrastructure and distributed systems

Job Summary

  • 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.
  • Wayve is committed to creating a diverse, fair and respectful culture that is inclusive of everyone based on their unique skills and perspectives.

Matching Summary

Match Score: 85

Wayve is seeking a Staff ML Performance Engineer to optimize large-scale machine learning jobs, enhancing the efficiency of training and inference workloads in the context of autonomous driving technology. The ideal candidate should have extensive experience in performance engineering, particularly in GPU compute infrastructure and distributed systems.

Skills & Requirements

Must-have

  • ML workload profiling
  • efficiency improvements
  • observability tools
  • benchmarking tools
  • high quality Python code

Nice-to-have

  • concurrent, parallel, and distributed computing
  • GPU kernel implementation
  • computing fundamentals knowledge

Key Requirements

  • 10+ years of industry experience
  • Experience optimizing large scale jobs on GPU compute clusters
  • Experience in platform and research teams
  • Experience writing and tracking performance benchmarks
  • BS or MS in Machine Learning, Computer Science, Engineering, or equivalent experience

Work Rights

Not specified

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