Senior Product Manager, Agent and ML Infrastructure

Senior Product Manager, Agent and ML Infrastructure

Google place Mountain View, CA, USA

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• Bachelor's degree or equivalent practical experience.
• 8 years of experience in product management or related technical role.
• 3 years of experience taking technical products from conception to launch (e.g., ideation to execution, 0 to 1, etc.).
• Experience developing/launching AI infrastructure or AI platform products

• Experience with applications to enable the ever growing needs of business partners and end customers.
• Experience working in a cross-functional organization, and influencing senior executives.
• Ability to apply cross-functional leadership to develop long‑term strategy.
• Excellent product management, business acumen, analytical, and stakeholder management skills, with attention to detail.

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.

The Customer Engagement (CE) team within Ads provides tools and products to enable Google to engage customers and help them succeed. The team’s solutions cover marketing, sales, and support for the Ads business and also customer support for Google’s consumer ecosystem. In this role, you will build and maintain the AI and data platforms, and will drive all ML‑driven experiences across external user‑facing and internal surfaces.

Google Ads is helping power the open internet with the best technology that connects and creates value for people, publishers, advertisers, and Google. We’re made up of multiple teams, building Google’s Advertising products including search, display, shopping, travel and video advertising, as well as analytics. Our teams create trusted experiences between people and businesses with useful ads. We help grow businesses of all sizes from small businesses, to large brands, to You Tube creators, with effective advertiser tools that deliver measurable results.

We also enable Google to engage with customers at scale.

The US base salary range for this full‑time position is $183,000–$271,000 + 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.

• Own and drive a multi‑year roadmap alongside engineering partners for serving and agent development that anticipates the needs of developers in the customer engagement ecosystem.
• Prioritize features that accelerate velocity, simplify agent deployment, and ensure responsible AI practices.
• Partner with engineering to optimize TPU allocation and serving infrastructure. Stay aligned with Google’s AI breakthroughs (e.g., Deep Mind, Core ML) to integrate capabilities into the platform.
• Articulate the value proposition, roadmap, and capabilities to audiences, including engineering, product, and leadership.
• Define and track critical KPIs (e.g., model launch times, resource efficiency, and successful agent deployments)…