Technical Program Manager III, Compute Services, Google Cloud

Minimum qualifications:

• Bachelor's degree in a technical field, or equivalent practical experience.
  
• 5 years of experience in program management.
  
• Experience in managing new product introduction (NPI) programs for AI infrastructure hardware.
• Experience managing machine learning infrastructure, including with network, compute and storage.

Preferred qualifications:

• 5 years of experience managing cross-functional or cross-team projects.
• Understanding of data center terminology and operations.
• Knowledge about software development processes and SCRUM.

About the job

Google's projects, like our users, span the globe and require managers to keep the big picture in focus while being able to dive into the unique engineering challenges we face daily. As a Technical Program Manager at Google, you lead complex, multi-disciplinary engineering projects using your engineering expertise. You plan requirements with internal customers and usher projects through the entire project lifecycle. This includes managing project schedules, identifying risks and clearly communicating them to project stakeholders. You're equally at home explaining your team's analyses and recommendations to executives as you are discussing the technical trade-offs in product development with engineers.

Using your extensive technical and leadership expertise, you manage projects of various size and scope, identifying future opportunities, improving processes and driving the technical directions of your programs.

We are looking for engineers with exposure to fully integrated AI infrastructure systems, including Graphics Processing Units (GPUs) or Tensor Processing Units (TPUs). This experience should span from Hardware (HW) to Software (SW) design, encompassing workload management, developing training and inference workloads, and optimizing performance. We are particularly interested in candidates with experience building AI clusters using the latest technologies for AI acceleration, cluster interconnects, and networking.The ML, Systems, and Cloud AI (MSCA) organization at Google designs, implements, and manages the hardware, software, machine learning, and systems infrastructure for all Google services (Search, YouTube, 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 + 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.

Responsibilities

• Lead the development and deployment of next-gen AI Infrastructure (Infra) products from concept to production, including: leading SW qualification and release strategy, managing test infrastructure, developing core management software, and building monitoring and diagnostics tooling.
• Define and manage delivery orchestration workflows.
• Collaborate with engineering, product, and stakeholders to ensure alignment on operational priorities and customer needs.
• Manage escalations and critical incidents, ensuring timely resolution and effective communication.
• Ensure that new feature requests and support actions are seamlessly integrated into the product/feature roadmaps, milestones, and engineering Objectives and Key Results (OKRs).