Senior Product Manager, On-Device Machine Learning, Infrastructure Pillar - Google
Sunnyvale, CA
About the Job
Minimum qualifications:
- Bachelor's degree or equivalent practical experience.
- 8 years of experience in product management or related technical role.
- 3 years of experience developing or launching products or technologies within artificial intelligence or Machine Learning (AI or ML).
Preferred qualifications:
- Master's degree in Computer Science, Computer Engineering, or equivalent practical experience.
- Experience with Android and iOS mobile development APIs and constraints.
- Experience with one or more aspects of ML acceleration (e.g., silicon architecture, design, ML compilers, tools/infrastructure, ML models, etc.).
- Experience leading cross-functional teams to identify opportunities, and drive innovation into consumer products.
- Ability to understand and mediate between customer requirements and engineering requirements/constraints.
- Excellent communication skills to influence initiatives with key stakeholders and executive management.
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.
Core ML’s mission is to drive ML excellence by building more reliable, easy to use, and flexible tools for ML practitioners so that customers can more easily experiment with new research ideas and deploy them at scale in production. The team's goal is to enable innovation and adoption of machine learning across products built by Google and by Google Cloud customers.
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 $168,000-$252,000 + 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
- Understand On-Device Machine Learning (ODML) market trends and current customer needs.
- Gather requirements and define workflows to ensure the ML runtime and infrastructure deliver optimal cross-ML-framework performance across CPU, GPU, and NPU on mobile, web, and embedded devices.
- Partner with engineering and product leaders within Core ML, ODML, and across organizational boundaries to help define and execute roadmaps, PRDs, and OKRs.
- Work with various OEMs and SoCs to integrate their accelerators and SDKs into runtime.
- Lead prioritized execution with a focus on customer needs, business outcomes, and success metrics.