Product Manager, Machine Learning Frameworks

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 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 full-life cycle product management and delivery.
• 2 years of experience with Artificial Intelligence/Machine Learning model or tooling development (e.g., Product Management or Engineering).
• 2 years of experience in engineering.

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 Core Machine Learning team builds the tooling, service, and technical foundations to enable machine learning research and production behind Google’s flagship products and Artificial Intelligence/Machine Learning Operational Support System (OSS) offerings. We are owners and advocates for the underlying design elements, developer platforms, components, and infrastructure at Google.

As a Product Manager, you will lead teams developing Machine Leraning Frameworks that enable developers to take advantage of models and innovation happening across internal and external Artificial Intelligence ecosystems. You will interact with teams developing JAX, Keras, PyTorch, serving technologies, and more. You will work with both internal Product Areas and also the external OSS community and will accelerate Machine Leraning innovation from Google research and Google product areas to all of Google’s products.

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 $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

• Understand the workflows of internal and external Machine Learning Developers.
  
• Engage with all Google product areas and the wider Operational Support System (OSS) community to understand their needs and build platform features to enable use cases.
  
• Drive roadmap development for machine learning stack tooling.
  
• Provide thought leadership on Machine Learning ecosystem strategy.
  
• Lead teams through defining, identifying, collecting, and tracking appropriate product or business metrics.