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Senior Staff Software Engineer, ML Performance, GPU

Google· Google Core ML / ML Performance
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Full-timePosted 4d ago

About the role

Google's Core ML organization seeks a Senior Staff Software Engineer to drive GPU performance optimizations for Gemini models, focusing on ML performance, GPU architectures, and integration with XLA, CUDA, Triton, and related tooling.

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 ML model performance challenges, such as onboarding new LLM models and products on GPU hardware and enabling LLMs to train and serve efficiently at 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.]
  • 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."],
  • benefitsFromJobDescriptionTextFormatRequestToBeParsedBeyondProvidedDetailsInfoOnly
  • domains:[{name":"AI/ML Systems" ,description":"Development of ML performance and GPU-accelerated infrastructure for large-scale AI models.",categories:["ai / ml"],stage":"production"}]
  • skills:[{"name":"GPU performance engineering","description":"Experience with GPU architectures, memory hierarchies, and optimization techniques.","category":"technical","requirement":"required","proficiency_level":"expert"},{"name":"Low-level GPU programming","description":"CUDA, Triton, CUTLASS, and related tooling for performance tuning.","category":"technical","requirement":"required","proficiency_level":"expert"},{"name":"LLM deployment on accelerators","description":"Experience deploying large language models on AI accelerators.","category":"technical","requirement":"required","proficiency_level":"expert"},{"name":"Performance benchmarking and analysis","description":"Headroom/roofline analysis, benchmarking, and optimization.","category":"technical","requirement":"required","proficiency_level":"expert"}]
  • tech_stacks:[{"name":"CUDA","description":"GPU programming framework","category":"framework","area":"infra","importance":"primary"},{"name":"Triton","description":"GPU kernel compiler/runtime","category":"framework","area":"infra","importance":"primary"},{"name":"CUTLASS","description":"CUDA templates for matrix multiply and more","category":"framework","area":"infra","importance":"secondary"},{"name":"XLA","description":"Accelerator-optimized compiler stack","category":"tool","area":"infra","importance":"primary"},{"name":"Gemini GPUs/AI accelerators","description":"Google’s GPU/AI accelerator stack","category":"platform","area":"infra","importance":"primary"}]}
  • locations:[{"description":"Sunnyvale, CA, USA","location_type":"onsite","location":"Sunnyvale, CA, USA"}],
  • compensations:[{"description":"US: $262000 - $364000 (USD) + 25% bonus target + equity + benefits","compensation_type":"base","min":262000,"max":364000,"unit":"USD","currency":"USD","period":"year"}]}
  • summary_raw_text_from_job_description_only_note':'IBM'}
  • additional_context_text_excerpt_for_schema_note_does_not_apply_for_required_fields_only_placeholder
Last updated Oct 23, 2026. Interested? Apply on the company site.

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