Senior Staff Software Engineer, ML Performance, GPU
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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.
About the job
Google's software engineers develop the next-generation technologies that change how billions of users connect, explore, and interact with information and one another. Our products need to handle information at massive scale, and extend well beyond web search. We're looking for engineers who bring fresh ideas from all areas, including information retrieval, distributed computing, large-scale system design, networking and data storage, security, artificial intelligence, natural language processing, UI design and mobile; the list goes on and is growing every day. As a software engineer, you will work on a specific project critical to Google’s needs with opportunities to switch teams and projects as you and our fast-paced business grow and evolve. We need our engineers to be versatile, display leadership qualities and be enthusiastic to take on new problems across the full-stack as we continue to push technology forward.
Google’s Core ML organization is seeking software engineers for the ML Performance team to drive GPU performance optimizations for Gemini models. Working in close partnership with DeepMind and XLA, the team also writes custom kernels in CUDA, Triton, and Pallas/Mosaic to achieve peak efficiency.
The ML Performance team focuses on performance analysis such as headroom/roofline analysis, benchmarking, and hill-climbing, along with advanced optimization techniques including sharding, compiler optimizations, quantization, custom kernels, and agentic optimizations. Beyond delivering immediate model-specific improvements, the team achieves scalable results by promoting these point optimizations into principled, stack-level enhancements across compilers and kernel libraries to boost out-of-the-box performance.
The AI and Infrastructure team is redefining what’s possible. We empower Google customers with breakthrough capabilities and insights by delivering AI and Infrastructure at unparalleled scale, efficiency, reliability and velocity. Our customers include Googlers, Google Cloud customers, and billions of Google users worldwide.
We're the driving team behind Google's groundbreaking innovations, empowering the development of our cutting-edge AI models, delivering unparalleled computing power to global services, and providing the essential platforms that enable developers to build the future. From software to hardware our teams are shaping the future of world-leading hyperscale computing, with key teams working on the development of our TPUs, Vertex AI for Google Cloud, Google Global Networking, Data Center operations, systems research, and much more.
Individual pay is determined by factors including job-related skills, experience, and relevant education or training.US: $262000 - $364000 (USD) + 25% bonus target + equity + benefits
Learn more about benefits at Google.
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.
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