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 Co-Design Engineer, you will focus and conduct LLM Serving Studies. In this role, you will work on analyzing and optimizing the serving performance of emerging models and use cases on our custom hardware. You will also work closely with hardware architects to influence the 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 . • Conduct comprehensive serving performance studies on (current and emerging) LLMs (1P/3P models). • Develop and maintain advanced simulation, profiling, and modeling tools to identify bottlenecks, understand key characteristics and project serving workload performance. • Partner with model researchers, software and hardware teams to co-design architectural improvements tailored to Large Language Model (LLM) inference latency and throughput. • Drive data-backed decisions that influence the roadmap for future TPU/Cloud Silicon architectures. • Bachelor’s degree in Computer Science, Electrical Engineering, Computer Engineering, a related field, or equivalent practical experience. • 5 years of experience in performance modeling/engineering, computer architecture, co-design, or systems engineering. • Experience programming in C++ or Python. Minimum qualifications: • Bachelor’s degree in Computer Science, Electrical Engineering, Computer Engineering, a related field, or equivalent practical experience. • 5 years of experience in performance modeling/engineering, computer architecture, co-design, or systems engineering. • Experience 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. • Experience with hardware/software co-design problems, especially performance analysis and identification at the pre-silicon stage. • Experience enabling and optimizing large-scale ML models (e.g., LLMs, large embedding models). • Experience with ML infrastructure, profiling tools, or deep learning inference/serving optimizations. • Familiarity with accelerator architectures.

Requirements

Must-haves

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.
Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
Learn more about benefits at Google .
Conduct comprehensive serving performance studies on (current and emerging) LLMs (1P/3P models).
Develop and maintain advanced simulation, profiling, and modeling tools to identify bottlenecks, understand key characteristics and project serving workload performance.
Partner with model researchers, software and hardware teams to co-design architectural improvements tailored to Large Language Model (LLM) inference latency and throughput.
Drive data-backed decisions that influence the roadmap for future TPU/Cloud Silicon architectures.
Bachelor’s degree in Computer Science, Electrical Engineering, Computer Engineering, a related field, or equivalent practical experience.
5 years of experience in performance modeling/engineering, computer architecture, co-design, or systems engineering.
Experience programming in C++ or Python.
Bachelor’s degree in Computer Science, Electrical Engineering, Computer Engineering, a related field, or equivalent practical experience.
5 years of experience in performance modeling/engineering, computer architecture, co-design, or systems engineering.
Experience 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.
Experience with hardware/software co-design problems, especially performance analysis and identification at the pre-silicon stage.
Experience enabling and optimizing large-scale ML models (e.g., LLMs, large embedding models).
Experience with ML infrastructure, profiling tools, or deep learning inference/serving optimizations.
Familiarity with accelerator architectures.

Application prep

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

Keywords to mirror

aimlmachine learningllmpython

Platform: google
Effort: medium