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Product Manager, ML Efficiency, ACE Infrastructure

Google • Full-time • Sunnyvale, California, United States • $156k - $229k / year • 7h ago

Minimum qualifications:

Bachelor's degree or equivalent practical experience.
5 years of experience in product management or related technical role.
2 years of experience developing or launching infrastructure products or technologies within networking, storage, compute hardware, databases, file systems, data analytics, cluster management, or other software infrastructure areas.
Experience with machine learning, efficiency measurement or metrics analysis.

Preferred qualifications:

Master's degree in a technology or business related field.
3 years of experience in a business function or role (e.g., marketing, business operations, consulting).
3 years of experience in a role preparing and delivering technical presentations to executive leadership.
2 years of experience in software development or engineering.
2 years of experience working cross-functionally with engineering, UX/UI, sales finance, and other stakeholders.
1 year of experience in technical leadership.

About the job

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 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 a part of the ACE (AI and Compute Enablement) Infrastructure team, you will execute Google's AI first goal. You will provide the fundamental compute infrastructure and resource management systems essential for developing everything from large language models to specialized AI applications. You will be a part of the ML efficiency team within ACE Infrastructure, focused on ensuring we hit company wide goals for utilization and efficiency across serving and training.Google Cloud accelerates every organization’s ability to digitally transform its business and industry. We deliver enterprise-grade solutions that leverage Google’s 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.

The US base salary range for this full-time position is $156,000-$229,000 + 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

Develop goals for Google’s ML infrastructure with a focus on efficiency and the right level of user abstraction.
Drive optimality in the hardware portfolio and ML fleet through thoughtful design of ML physical systems, and by ensuring that hardware designs lend themselves to efficient usage and performance optimization.
Drive coherence between Hardware (HW) and Software (SW) development programs to enable most productive use of the systems as deployed.
Develop effective partnerships with Google’s product areas (e.g., Search, Ads, YouTube, Cloud etc.) and understand their evolving needs for learning, training and serving to improve developer experiences, and enable productive use of Google’s ML resources.
Own and set the strategy, roadmap, and implementation for multiple areas of focus, balancing short and long-term priorities against various business needs.

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