Senior Staff Software Engineer, ML Performance, GPU

About the role

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

[https://www.google.com/about/careers/applications/benefits/].

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.