Technical Program Manager III, NPI Test Engineering, Cloud Infrastructure
Job Description
- 5+ years managing cross-functional projects
- Experience with Rack Integration and ICT testing
- Experience with ML or semiconductor technologies and testing
- Experience as a technical program manager in software and hardware development
- Bachelor's degree in technical field or equivalent experience
- 5+ years program management experience
- Experience in test engineering (software & hardware)
- Manage project plans, timelines, and budgets
- Collaborate with ML engineers and product managers
- Bonus
- Equity
- Benefits
Minimum qualifications:
- Bachelor's degree in a technical field, or equivalent practical experience.
- 5 years of experience in program management.
- Experience in test engineering (e.g., software and hardware testing principles and methodologies).
Preferred qualifications:
- 5 years of experience managing cross-functional or cross-team projects.
- Experience with Rack Integration testing and ICT testing processes.
- Experience with Machine Learning or semi-conductor technologies and testing.
- Experience as a technical program manager in software and hardware development environments.
- Experience managing technical projects, including planning, execution, and delivery.
About the job
A problem isn’t truly solved until it’s solved for all. That’s why Googlers build products that help create opportunities for everyone, whether down the street or across the globe. As a Technical Program Manager at Google, you’ll use your technical expertise to lead complex, multi-disciplinary projects from start to finish. You’ll work with stakeholders to plan requirements, identify risks, manage project schedules, and communicate clearly with cross-functional partners across the company. You're equally comfortable explaining your team's analyses and recommendations to executives as you are discussing the technical tradeoffs in product development with engineers.
Within Product Lifecycle and Technical Operations (PLaTO), the Central Test Engineering organization is focused on guiding and empowering the manufacturing test engineering teams across product verticals and business segments to develop, deploy, and manage test solutions that meet the quality, cost, and time-to-market needs of Google Cloud products. The team is responsible for building new technologies and processes that support factory test operations to be scalable and portable across the supply chain.
Behind everything our users see online is the architecture built by the Technical Infrastructure team to keep it running. From developing and maintaining our data centers to building the next generation of Google platforms, we make Google's product portfolio possible. We're proud to be our engineers' engineers and love voiding warranties by taking things apart so we can rebuild them. We keep our networks up and running, ensuring our users have the best and fastest experience possible.
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 .
Responsibilities
- Develop and manage comprehensive project plans, timelines, and budgets for test development initiatives related to the ML roadmap, with a focus on Rack Integration test and ICT test.
- Collaborate with ML engineers and product managers to understand test requirements and translate them into test strategies.
- Assist in the development and implementation of new test strategies and technologies aligned with organizational goals.
- Coordinate board and system-level test development activities and dependencies for Machine Learning hardware and software systems across internal teams and external partners.
- Drive communication with internal stakeholders and other partners for ML test development status via reviews and dashboards, and work with ML design engineering teams to manage dependencies for test development.
