Product Manager, Machine Learning Infrastructure - Google
Sunnyvale, CA
About the Job
Minimum qualifications:
- Bachelor's degree or equivalent practical experience.
- 5 years of experience in product management or a related technical role.
- 3 years of experience building and shipping technical products.
- 2 years of experience developing or launching products or technologies within software as a service (SaaS), or a related area.
- Experience within Cloud Infrastructure, particularly Machine Learning Infrastructure, with compute, networking and storage.
Preferred qualifications:
- MBA or advanced technical degree.
- Experience managing products with one of the targeted workloads, such as Machine Learning, training and deploying large machine learning models, or HPC, deploying hardware and software needed to run large batch compute jobs.
- Experience leading complex strategic and operational initiatives, working through technical, operational, legal/policy, and business issues.
- Understanding of the AI/ML software stack and Machine Learning frameworks such as PyTorch, TensorFlow, JAX, etc.
- Excellent communication and presentation skills.
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.
In this role, you will be responsible for developing an AI/ML infrastructure platform that could have transformative effects on the Generative AI and Large Language Model ecosystems, and an outsized impact on the Google Cloud business.
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
- Understand workflows of internal and external experts for workloads, specifically large-scale AI/ML training, and inference.
- Understand where internal and external experts are running into limits, know how our products can best meet their needs, and shape our product roadmap by building new offerings to best drive growth to Google Cloud Platform (GCP).
- Work with directors and executives across Google to ensure effective product launches and experience.
- Drive product strategy and set product priorities, teaming closely with Engineering and cross-functional teams to define and deliver on the next generation of cloud infrastructure services.
- Understand the cloud Machine Learning infrastructure customer, their unique requirements, and are well versed with foundational Large Language Machine Learning models, their infrastructure and toolkit dependencies, and technical challenges to elastically scaling them.