Staff Software Engineer, GPU Performance

Google

full-time

Required skills

CUDA
Triton
CUTLASS
MLIR
OpenXLA
TRT-LLM
vLLM
GPU architectures
SGLang

About the role

In accordance with Washington state law, we are highlighting our comprehensive benefits package, which is available to all eligible US based employees. Benefits for this role include:

Health, dental, vision, life, disability insurance
Retirement Benefits: 401(k) with company match
Paid Time Off: 20 days of vacation per year, accruing at a rate of 6.15 hours per pay period for the first five years of employment
Sick Time: 40 hours/year (increased to 69 hours/year for Seattle) including 5 discretionary sick days per instance
Maternity Leave (Short-Term Disability + Baby Bonding): 28-30 weeks
Baby Bonding Leave: 18 weeks
Holidays: 13 paid days per year

Note: By applying to this position you will have an opportunity to share your preferred working location from the following: Sunnyvale, CA, USA; Kirkland, WA, USA; New York, NY, USA.

Minimum qualifications:

Bachelor’s degree or equivalent practical experience.
8 years of experience in software development.
5 years of experience testing, and launching software products, and 3 years of experience with software design and architecture.
Experience with modern GPU architectures (NVIDIA, AMD, or other AI accelerators), memory hierarchies, and performance bottlenecks.
Experience with modern LLMs and their deployment on AI accelerators.
Experience with low-level GPU programming (CUDA, Triton, CUTLASS, etc.) and performance engineering techniques.

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 structured organization involving cross-functional, or cross-business projects.
Experience with compiler optimization, code generation, and runtime systems for GPU architectures (OpenXLA, MLIR, Triton, etc.).

About the job

Google Cloud’s mission is to make every business successful through AI by combining cutting-edge technology, infrastructure, and talent. AI/ML software engineers in Cloud bridge the gap between pioneering models and a massive product vehicle reaching billions. Our talent density and AI-powered tools drive rapid development, rooted in a culture of empowerment and a bias to action. In this role, you aren’t just building technology; you’re shaping the frontier of enterprise and driving the evolution of advanced models.

While known for pioneering work with TPUs, GPUs are an equally vital and rapidly expanding frontier within Google's machine learning infrastructure. GPUs are indispensable to Google’s diverse and ever-evolving landscape for strategic, pragmatic, and performance-driven reasons ensuring top performance for our machine learning (ML) models, adapting to ML workloads, achieving results, and influencing next-gen GPU architectures via partnerships.

In recognition of hardware as a strength, Google’s Core ML organization is heavily invested in growing the powerhouse team of GPU experts, and we invite you to be at its vanguard.

In this role, you will have the opportunity to move beyond incremental improvements and architect transformative solutions, shaping the future of AI and accelerated computing for Google and the world.

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 force 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.

The US base salary range for this full-time position is $207,000-$300,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process.

Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits. Learn more about benefits at Google.

Responsibilities

Identify and maintain LLM training and serving benchmarks, using them to identify performance opportunities, drive XLA:GPU/Triton performance toward XLA releases.
Engage with various teams, like DeepMind, to solve challenging ML model performance problems.
Run architecture-level simulations on GPU designs and perform roofline analysis to guide partner teams.
Analyze performance and efficiency metrics to identify bottlenecks and then design and implement solutions at Google fleet-wide scale.
Run performance benchmarks on GPU hardware using internal and external tools such as TRT-LLM, vLLM , and SGLang.

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 Google's EEO Policy and EEO is the Law. If you have a disability or special need that requires accommodation, please let us know by completing our Accommodations for Applicants form.

About Google

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