Staff Software Engineer, ML Performance, GPU

Staff Software Engineer, ML Performance, GPU

Share Staff Software Engineer, ML Performance, GPU

corporate_fare Google place Sunnyvale, CA, USA

Advanced

Experience owning outcomes and decision making, solving ambiguous problems and influencing stakeholders; deep expertise in domain.

Share Staff Software Engineer, ML Performance, GPU

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XIn most instances, this position requires in-person interviews as part of the hiring process.

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.

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.

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 aboutbenefits at Google (https://www.google.com/about/careers/applications/benefits/) .

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.

Information collected and processed as part of your Google Careers profile, and any job applications you choose to submit is subject to Google'sApplicant and Candidate Privacy Policy (./privacy-policy) .

Google is proud to be an equal opportunity and affirmative action employer. We are committed to building a workforce that is representative of the users we serve, creating a culture of belonging, and providing an equal employment opportunity regardless of race, creed, color, religion, gender, sexual orientation, gender identity/expression, national origin, disability, age, genetic information, veteran status, marital status, pregnancy or related condition (including breastfeeding), expecting or parents-to-be, criminal histories consistent with legal requirements, or any other basis protected by law. See alsoGoogle's EEO Policy (https://www.google.com/about/careers/applications/eeo/) ,Know your rights: workplace discrimination is illegal (https://careers.google.com/jobs/dist/legal/EEOC\_KnowYourRights\_10\_20.pdf) ,Belonging at Google (https://about.google/belonging/) , andHow we hire (https://careers.google.com/how-we-hire/) .

If you have a need that requires accommodation, please let us know by completing ourAccommodations for Applicants form (https://goo.gl/forms/aBt6Pu71i1kzpLHe2) .

Google is a global company and, in order to facilitate efficient collaboration and communication globally, English proficiency is a requirement for all roles unless stated otherwise in the job posting.

To all recruitment agencies: Google does not accept agency resumes. Please do not forward resumes to our jobs alias, Google employees, or any other organization location. Google is not responsible for any fees related to unsolicited resumes.

Equity is granted exclusively and discretionarily by Alphabet Inc. on the basis of an agreement concluded between you and Alphabet Inc. Alphabet Inc. is your sole contractual partner with respect to equity grants. GSU grants are not guaranteed, are discretionary, are subject to approval by the Alphabet Inc. board of directors or its delegate, the terms of the relevant Alphabet Inc. stock plan, and your grant agreement. They have no impact on statutory payments. Current or past grants do not confer an acquired right.

Google is proud to be an equal opportunity workplace and is an affirmative action employer. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. See also https://careers.google.com/eeo/ and https://careers.google.com/jobs/dist/legal/OFCCP_EEO_Post.pdf If you have a need that requires accommodation, please let us know by completing our Accommodations for Applicants form: https://goo.gl/forms/aBt6Pu71i1kzpLHe2.