Profile ML workloads to identify bottlenecks and optimize training workloads
Improve MFU and throughput via parallelism, model compilation, and mixed precision
Build observability tools to track MFU, throughput, and latency
Develop benchmarking tools to log efficiency gains or regressions
Collaborate with Research teams to scale training efficiency

🎯 Requirements

10+ years in performance engineering for ML systems or related fields
Experience optimizing large-scale GPU compute jobs
Experience with platform teams and research collaborations
Proven ability to report and track performance benchmarks
Strong Python programming skills
BS or MS in ML, CS, Eng, or related field

🎁 Benefits

Hybrid work in Sunnyvale, CA
Competitive equity package
Salary range: $336,400 - $359,000 (USD)

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