Job description

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. 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. Behind everything our users see online is the architecture built by the Technical Infrastructure team to keep it running. From developing and maintaining our data centers to building the next generation of Google platforms, we make Google's product portfolio possible. We're proud to be our engineers' engineers and love voiding warranties by taking things apart so we can rebuild them. We keep our networks up and running, ensuring our users have the best and fastest experience possible. Individual pay is determined by factors including job-related skills, experience, and relevant education or training. US: $207000 - $300000 (USD) + 20% bonus target + equity + benefits Learn more about benefits at Google . • 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. • Bachelor’s degree or equivalent practical experience. • 8 years of experience in software development. • 5 years of experience with ML design and ML infrastructure (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. Minimum qualifications: • Bachelor’s degree or equivalent practical experience. • 8 years of experience in software development. • 5 years of experience with ML design and ML infrastructure (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. • 3 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 in hardware-aware algorithm design and compiler stacks (e.g., OpenXLA), tailoring large-scale ML models and distributed systems for peak performance across accelerator hardware.

Requirements

Must-haves

Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
Learn more about benefits at Google .
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.
Bachelor’s degree or equivalent practical experience.
8 years of experience in software development.
5 years of experience with ML design and ML infrastructure (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.
Bachelor’s degree or equivalent practical experience.
8 years of experience in software development.
5 years of experience with ML design and ML infrastructure (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.
8 years of experience with data structures and algorithms.
3 years of experience in a technical leadership role leading project teams and setting technical direction.

Nice-to-haves

US: $207000 - $300000 (USD) + 20% bonus target + equity + benefits
Master’s degree or PhD in Engineering, Computer Science, or a related technical field.
Experience in hardware-aware algorithm design and compiler stacks (e.g., OpenXLA), tailoring large-scale ML models and distributed systems for peak performance across accelerator hardware.

Application prep

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Keywords to mirror

mlllmmachine learningai

Platform: google
Effort: medium