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Senior Staff Software Engineer, ML Performance, GPU

Google
Sunnyvale, USA
Senior · 8+ years experience
Onsite
USD 262000-364000 / year
Discovered 6 days ago

Full Job Posting

Responsibilities

  • Identify and maintain LLM training and serving benchmarks, use these to identify performance opportunities, drive XLA:GPU/Triton performance toward state-of-the-art, and guide XLA releases.
  • Collaborate with DeepMind to solve their ML model performance challenges, such as onboarding new LLM models and products on GPU hardware and enabling LLMs to train and serve efficiently on a very large scale.
  • Run architecture level simulations on GPU designs, perform roofline analysis to guide internal teams, and explore novel model/data efficiency techniques.
  • Analyze performance and efficiency metrics to identify bottlenecks, design, and implement solutions at Google fleet-wide scale.
  • Collaborate with tooling and fleet metrics sub-teams to build tools that track performance and efficiency and extract metrics from Google’s running workloads.

Minimum qualifications:

  • Bachelor’s degree or equivalent practical experience.
  • 8 years of experience in software development.
  • 7 years of experience leading technical project strategy, ML design and optimizing end-to-end Machine Learning (e.g., model deployment, model evaluation, data processing, debugging, fine tuning).
  • Experience with modern GPU architectures, memory hierarchies, and performance bottlenecks.
  • Experience with low-level GPU programming (CUDA, Triton, CUTLASS, etc.) and performance engineering techniques.
  • Experience with modern LLMs and their deployment on AI accelerators.

Preferred qualifications:

  • Master’s degree or PhD in Engineering, Computer Science, or a related technical field.
  • 8 years of experience with data structures and algorithms.
  • 5 years of experience in a technical leadership role leading project teams and setting technical direction.
  • 3 years of experience working in a complex, matrixed organization involving cross-functional, or cross-business projects.
  • Experience with machine learning algorithms and tools.

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