Staff Software Engineer, ML Performance, GPU

Staff Software Engineer, ML Performance, GPUGoogle'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.While known for pioneering work with TPUs, GPUs are an equally vital and rapidly expanding frontier within Google's ML infrastructure. GPUs are indispensable to Google's ever-evolving landscape for strategic, pragmatic, and performance-driven reasons — ensuring top performance for our ML models, adapting to ML workloads, achieving results, and influencing next-gen GPU architectures via partnerships.Core ML's GPU Performance team is responsible for optimizing, modeling, and evaluating GPU systems for comparative analysis and benchmarking for internal and external ML workloads. Our team's focus on performance analysis and optimization identifies opportunities in Google production and research ML workloads and lands optimizations to entire fleet.

We evaluate current and future ML workloads and runs performance/total cost of ownership simulations to collect roofline estimates and guide decision-making for the hardware teams.Responsibilities:Identify and maintain LLM training and serving benchmarks; use them to identify performance opportunities, drive XLA:GPU/Triton performance and guide XLA releases.Partner with product teams (e.g., Google DeepMind) to onboard, optimize, and scale LLMs and machine learning models on GPU hardware.Conduct architecture-level simulations, performance benchmarking, and roofline analyses using tools like TRT-LLM, vLLM, and SGLang to guide system designs.Analyze fleet-wide performance and efficiency metrics to identify bottlenecks and engineer scalable optimizations across Google's infrastructure.Research and implement model/data efficiency techniques, tooling, and profiling mechanisms to improve workload performance and training efficiency.Individual pay is determined by factors including job-related skills, experience, and relevant education or training. US: $ - $ (USD) + 20% bonus target + equity + benefits.