Product Manager II, ML Fleet Training

At Google, we put our users first. The world is always changing, so we need Product Managers who are continuously adapting and excited to work on products that affect millions of people every day.

In this role, you will work cross-functionally to guide products from conception to launch by connecting the technical and business worlds. You can break down complex problems into steps that drive product development.

One of the many reasons Google consistently brings innovative, world-changing products to market is because of the collaborative work we do in Product Management. Our team works closely with creative engineers, designers, marketers, etc. to help design and develop technologies that improve access to the world's information. We're responsible for guiding products throughout the execution cycle, focusing specifically on analyzing, positioning, packaging, promoting, and tailoring our solutions to our users.

As the Product Manager for ML Training Fleet Efficiency, you will serve as the product lead driving utilization, efficiency, and governance across Google's global AI accelerator fleet. In this role, you will direct cross-functional engineering initiatives across core infrastructure. You will act as an authoritative spokesperson and advisor to executive leadership to sustainably power Google's next-generation AI breakthroughs.

Google Cloud accelerates every organization’s ability to digitally transform its business and industry. We deliver enterprise-grade solutions that leverage Google’s cutting-edge technology, and tools that help developers build more sustainably. Customers in more than 200 countries and territories turn to Google Cloud as their trusted partner to enable growth and solve their most critical business problems.Individual pay is determined by factors including job-related skills, experience, and relevant education or training.

US: $163000 - $236000 (USD) + 15% bonus target + equity + benefits

Learn more about benefits at Google.

• Define and execute the multi-year product roadmap for Google’s ML Training Fleet Efficiency across TPUs, targeting high-leverage bottlenecks and unlocking hundreds of millions in compute capacity.
• Standardize company-wide efficiency metrics (such as Prod System Duty Cycle/PSDC and PADC) and telemetry architectures to deliver actionable visibility across executive and engineering stakeholders.
• Partner with DeepMind and Core ML to optimize complex distributed training workloads, specifically targeting Reinforcement Learning, data ingestion pipelines, and check-pointing efficiency.
• Drive the productization of "AI for AI" diagnostic platforms (e.g., agentic root-cause analysis and trace telemetry) to automate detractor resolution at fleet scale.
• Lead resource governance and quota frameworks across Search, Ads, Cloud, and GDM, fostering high-trust executive partnerships that maximize accelerator utilization without compromising research velocity.

Minimum qualifications:

• Bachelor's degree or equivalent practical experience.
  
• 5 years of experience in product management or related technical role.
  
• 2 years of experience taking technical products from conception to launch (e.g., ideation to execution, end-to-end, 0 to 1, etc.).
• Experience utilizing AI productivity tools to streamline individual, business, or development workflows.

Preferred qualifications:

• Master’s degree or PhD in Computer Science, Computer Engineering, or an MBA with a technical undergraduate degree.
• 5 years of experience managing machine learning infrastructure, AI accelerator platforms (TPUs/GPUs), or high-performance computing (HPC) clusters.
• 3 years of experience developing and deploying AI systems, managing machine learning infrastructure, or evaluating and optimizing AI models for task execution and reliability.
• Deep familiarity with modern machine learning training and post-training frameworks (JAX, PyTorch, Ray), distributed tracing, and data ingestion architectures.
• Demonstrated success establishing company-wide metric standards, capacity governance models, or developer observability platforms.