Product Manager II, AI Hardware and Software Co-design

corporate_fareGoogleplaceMountain View, CA, USA; New York, NY, USA; +2 more; +1 more

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 with AI/ML foundations, LLM/ML serving infrastructure, hardware accelerators, or silicon development.
• Experience working cross-functionally with engineering, research, and infrastructure teams to launch technical products.

Preferred qualifications:

• Master’s degree or PhD in Computer Science, Electrical Engineering, or a related technical field.
• Ability to negotiate and resolve complex technical hardware, software, and model co-design tradeoffs.
• Ability to narrate a compelling story that articulates the motivation, goals, and path to success for technical and non-technical stakeholders alike.
• Ability to build strategic relationships and project future customer needs in ambiguous landscapes.
• Knowledge of AI/ML architectures (e.g., transformers, mixture of experts) and inference performance metrics (e.g., TCO, TT80T, QPS).
• Knowledge of the silicon/hardware development lifecycle (e.g., EVT, DVT, PVT) and data center deployment constraints.

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 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 Research Compute Systems team in Google Research works at the frontier of AI hardware-software co-design across Google. The team's mission is to improve key compute system metrics through co-design across the AI stack (e.g., from data center to chips to model to application).

The role involves both an ownership-mentality of products and programs while also a willingness to pitch in wherever needed to move projects forward. In the early phases, you will help define requirements and de-risk experimental paths. In mid-to-later phases, the role requires deep engagement with internal and external customers, developing quick understanding of their workloads, helping to negotiate strict technical tradeoffs, and driving the product through critical hardware phases to pilot and general availability.

The US base salary range for this full-time position is $163,000-$237,000 + 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.

Responsibilities

• Define and execute the product strategy for your designated focus area, making decisions about areas of investment and prioritizing/deprioritizing features to keep cross-functional teams focused on high-impact outcomes.
• Partner with internal product areas and external customers to anticipate and synthesize complex future requirements, translating them into hardware and software specifications.
• Direct the resolution of difficult, ambiguous problems (e.g., model quality vs. latency) through evidenced-based, data-driven arguments.
• Collaborate closely with engineering to determine optimal technical implementation and scheduling. Drive the hardware through physical milestones and establish project processes to ensure alignment across distributed teams.
• Utilize AI to enhance your personal impact, identify new ultra-specialized product opportunities, and maintain a comprehensive working knowledge of the AI-powered product development lifecycle.