Technical Program Manager III, Cloud Infrastructure, Cloud AI Systems

Technical Program Manager III, Cloud Infrastructure, Cloud AI Systems

Kirkland, WA, USA;
Sunnyvale, CA, USA

Mid

Experience driving progress, solving problems, and mentoring more junior team members; deeper expertise and applied knowledge within relevant area.

• Bachelor’s degree in a technical field, or equivalent practical experience.
• 5 years of experience in program management.
• Experience building, automating, and developing distributed systems.
• Experience with public cloud infrastructure platforms (ex. Google Cloud Platform (GCP), Cloud Computing Platform, etc.).

• 5 years of experience managing cross-functional or cross-team projects.
• Experience with IaaS, virtual machine management, and distributed control plane issues.
• Experience with software and system reliability, scalability, and performance.
• Experience with Google Compute Engine (GCE) architecture and its control plane components.
• Experience with API design, microservices, and database technologies.

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 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.

As a part of Google Cloud’s Infrastructure‑as‑a‑Service (IaaS) offering, Google Compute Engine (GCE) provides scalable and flexible virtual machines and associated services. You will ensure the reliability, availability, and resilience of the GCE Control Plane, enabling customers to effectively manage their cloud infrastructure and applications. The ML, Systems, & Cloud AI (MSCA) organization at Google designs, implements, and manages the hardware, software, machine learning, and systems infrastructure for all Google services (Search, You Tube, etc.)

and Google Cloud. Our end users are Googlers, Cloud customers and the billions of people who use Google services around the world.

We prioritize security, efficiency, and reliability across everything we do – from developing our latest TPUs to running a global network, while driving towards shaping the future of hyperscale computing. Our global impact spans software and hardware, including Google Cloud’s Vertex AI, the leading AI platform for bringing Gemini models to enterprise customers.

The US base salary range for this full‑time position is $156,000-$229,000. Bonus, equity and benefits are additional. Your recruiter can share more about the specific salary range for your preferred location during the hiring process.

Google is proud to be an equal opportunity and affirmative action employer. We are committed to building a workforce that is representative of the users we serve, creating a culture of belonging, and providing an equal employment opportunity regardless of race, creed, color, religion, gender, sexual orientation, gender identity/expression, national origin, disability, age, genetic information, veteran status, marital status, pregnancy or related condition (including breastfeeding), expecting or parents‑to‑be, criminal histories consistent with legal requirements, or any other basis protected by law.

We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. Please note that if you have a need that requires accommodation, complete our Accommodations for Applicants form.

• Lead and manage cross‑functional programs for the Google Cloud Engine Control Plane, encompassing feature development, architectural enhancements, scalability improvements, and reliability initiatives.
• Partner with engineering, product management, and site reliability engineering to define program scope, objectives, requirements, and success metrics.
• Identify and manage program risks, dependencies, and issues, implementing mitigation strategies as needed.
• Facilitate technical discussions, decision‑making, and trade‑off analysis.
• Develop and maintain program plans, schedules, tracking progress against milestones and ensuring timely delivery.