Product Manager, AI/ML, Google Cloud

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.
  
• Experience developing or launching products or technologies within Artificial Intelligence or Machine Learning (AI or ML).

Preferred qualifications:

• Master's degree in a technology or business related field.
  
• 3 years of experience in a business function or role (e.g., strategic marketing, business operations, consulting).
  
• 3 years of experience in a role preparing and delivering technical presentations to senior 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 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.

XLA is Google's ML compiler, one of the world's preeminent ML Software applications, and is used to run all of Google's internal workloads.

We work very closely with all of Google's internal ML teams - DeepMind/Gemini, Ads, Cloud, Bard, Magi, YouTube, as well as with some of the largest external foundation model training teams.

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.

The US base salary range for this full-time position is $142,000-$211,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. The range displayed on each job posting reflects the minimum and maximum target salaries for the position across all US locations. 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 the strategic vision for major components of Google’s critical ML hardware stack, including incorporating cutting-edge ML innovations and delivering state-of-the-art performance for internal and major Cloud customers.
• Engage with Google's largest ML product areas (DeepMind, Cloud, Search, Ads, YouTube, and others) and Cloud's largest ML-focused customers to inform Google's ML Software approach to identify the most impactful areas for XLA development.
• Work closely with ML Hardware NPI teams, customer engineering, and software partners to ensure our roadmap is timely and delivers planned NPI and customer impact.
• Drive coherence between hardware and software development programs to enable most productive use of our ML hardware.
• Develop effective partnerships with associated ML Software teams, including CoreML Frameworks (TensorFlow, JAX, PyTorch) and Cloud GPU/TPU, and understand their evolving needs for large-scale Generative AI training and serving.