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Performance Engineer

280k – 850kSan Francisco, CANew York, NYSeattle, WAHybrid
Summary

Performance Engineer optimizes throughput and robustness of large-scale ML distributed systems by solving novel performance issues. Requires significant software engineering experience at supercomputing scale and interest in ML.

About the role

You may be a good fit if you:

  • Have significant software engineering or machine learning experience, particularly at supercomputing scale
  • Are results-oriented, with a bias towards flexibility and impact
  • Pick up slack, even if it goes outside your job description
  • Enjoy pair programming (we love to pair!)
  • Want to learn more about machine learning research
  • Care about the societal impacts of your work

Strong candidates may also have experience with:

  • High performance, large-scale ML systems
  • GPU/Accelerator programming
  • ML framework internals
  • OS internals
  • Language modeling with transformers

Representative projects:

  • Implement low-latency high-throughput sampling for large language models
  • Implement GPU kernels to adapt our models to low-precision inference
  • Write a custom load-balancing algorithm to optimize serving efficiency
  • Build quantitative models of system performance
  • Design and implement a fault-tolerant distributed system running with a complex network topology
  • Debug kernel-level network latency spikes in a containerized environment
Skills
Machine LearningGPU ProgrammingDistributed SystemsML FrameworksOS InternalsTransformersKubernetesLoad BalancingPerformance OptimizationHigh-Throughput Systems
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