Job description

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. The TPU Chip Architecture and Performance Co-design team is at the forefront of optimizing Google's custom AI silicon for next-generation machine learning models. As a Senior Performance Engineer, you will specialize in LLM training studies to shape the hardware and software architectures that will train the world's most capable AI models. You will analyze training workloads for first-party (1P) and third-party (3P) models to drive the next evolution of Google's custom ML accelerators. 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. Individual pay is determined by factors including job-related skills, experience, and relevant education or training. US: $174000 - $252000 (USD) + 15% bonus target + equity + benefits Learn more about benefits at Google . • Lead hardware/software co-design and performance modeling for LLM training workloads across current and future generation silicon. • Analyze distributed training bottlenecks (compute, memory, networking) for massively scaled 1P and 3P models. • Build and enhance modeling infrastructure, simulators, and performance tooling tailored to distributed ML training. • Drive data-backed decisions that influence the roadmap for future TPU/Cloud Silicon architectures. • Work closely with ML research and compiler teams to optimize training algorithms and influence future hardware design. • Bachelor’s degree in Computer Science, Electrical Engineering, Computer Engineering, or a related field or equivalent practical experience. • 5 years of experience in performance modeling, computer architecture, or hardware/software co-design. • Experience with programming in C++ or Python. Minimum qualifications: • Bachelor’s degree in Computer Science, Electrical Engineering, Computer Engineering, or a related field or equivalent practical experience. • 5 years of experience in performance modeling, computer architecture, or hardware/software co-design. • Experience with programming in C++ or Python. Preferred qualifications: • Master's degree or PhD in Electrical Engineering, Computer Engineering or Computer Science, with an emphasis on computer architecture. • Deep understanding of distributed ML training methodologies (data, tensor, and pipeline parallelism).

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 .
Lead hardware/software co-design and performance modeling for LLM training workloads across current and future generation silicon.
Analyze distributed training bottlenecks (compute, memory, networking) for massively scaled 1P and 3P models.
Build and enhance modeling infrastructure, simulators, and performance tooling tailored to distributed ML training.
Drive data-backed decisions that influence the roadmap for future TPU/Cloud Silicon architectures.
Work closely with ML research and compiler teams to optimize training algorithms and influence future hardware design.
Bachelor’s degree in Computer Science, Electrical Engineering, Computer Engineering, or a related field or equivalent practical experience.
5 years of experience in performance modeling, computer architecture, or hardware/software co-design.
Experience with programming in C++ or Python.
Bachelor’s degree in Computer Science, Electrical Engineering, Computer Engineering, or a related field or equivalent practical experience.
5 years of experience in performance modeling, computer architecture, or hardware/software co-design.
Experience with programming in C++ or Python.

Nice-to-haves

US: $174000 - $252000 (USD) + 15% bonus target + equity + benefits
Master's degree or PhD in Electrical Engineering, Computer Engineering or Computer Science, with an emphasis on computer architecture.
Deep understanding of distributed ML training methodologies (data, tensor, and pipeline parallelism).

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aimlmachine learningllmpython

Platform: google
Effort: medium