Technical Program Manager II, Data Center Machine Learning, Cloud Supply Chain

Minimum qualifications:

• Bachelor's degree or equivalent practical experience.
• 2 years of experience managing projects.
• Experience working in data center operations.
• Experience working with Machine Learning.

Preferred qualifications:

• 2 years of experience managing cross-functional or cross-team projects.
• Experience with Electrical Engineering.
• Experience working with Network Architecture.
• Experience working with Product Quality.

About the job

In this role, you will work with Technical Leads, Engineering, Supply Chain, Data Center Operations, and other contributors and stakeholders to define quality goals, monitor metrics, initiate corrective and preventative actions as needed, and regularly report quality status to leadership and cross-functional teams. You will also drive Google Data Center machine and rack infrastructure quality through managing New Product Introduction Deployments, Builds, and Quality, and assure continuous improvements in Quality after full production entry.

The US base salary range for this full-time position is $132,000-$189,000 + 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.

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

• Support Machine Learning (ML) or Networking New Product Introductions.
• Set quality goals, track key quality, and reliability metrics.
• Work with stakeholders to meet or exceed quality goals.
• Guide data analysis effort in support of quality goals. Work with analytics and engineering teams.
• Partner with Supply Chain, Logistics, Server Operations, Reliability and Product and Supplier Quality teams to drive quality initiatives.