The role
From Google's own posting.
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.
In this role, you will be an excellent leader and will have experience in managing complex system programs across organizational boundaries; driving goals/objectives, associated program plans for ecosystem readiness, timelines, resolving problems, identifying gaps and highlighting them.
The AI and Infrastructure team is redefining what’s possible. We empower Google customers with breakthrough capabilities and insights by delivering AI and Infrastructure at unparalleled scale, efficiency, reliability and velocity. Our customers include Googlers, Google Cloud customers, and billions of Google users worldwide.
We're the driving force behind Google's groundbreaking innovations, empowering the development of our cutting-edge AI models, delivering unparalleled computing power to global services, and providing the essential platforms that enable developers to build the future. From software to hardware our teams are shaping the future of world-leading hyperscale computing, with key teams working on the development of our TPUs, Vertex AI for Google Cloud, Google Global Networking, Data Center operations, systems research, and much more.
Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
US: $240000 - $333000 (USD) + 25% bonus target + equity + benefits
Learn more about benefits at Google .
Lead strategic planning framework for a complex program portfolio including direct influence over resourcing decisions, planning cadence, and planning stakeholders.
Lead and program manage e2e engineering development and landing of next-gen ML TPU systems through NPI PLC process from concept to production and sustaining.
Engage with senior executives and cross functional leaders to strategize and contribute to ML hardware/system roadmap.
Partner with engineering leads and cross-functional teams to ensure that organization-wide Objectives and Key Results (OKRs) are set and reflect top-level strategy as well as ensure projects are effectively prioritized, resourced, and managed.
Ensure all leadership/stakeholders are aware of progress/blockers on a continuous basis, drive strategic and tactical decisions throughout all organizations and levels.
Minimum qualifications:
Bachelor's degree in a a technical field, or equivalent practical experience.
10 years of experience in program management.
5 years of experience leading hardware/software New Product Introduction (NPI) lifecycles for machine learning infrastructure (e.g., TPU, GPU, or similar AI-compute clusters).
Preferred qualifications:
10 years of experience managing complex cross-functional or cross-team projects.
Ability to align, prioritize, resource, and scale programs according to strategic objectives, and to result in maximized organizational benefits and improved business outcomes.
Ability to assess engineering problems, understand the interplay between program and technical issues, and contribute to shaping technical directions.
Ability to drive complex program level or organizational changes, and manage, communicate and introduce them in the least disruptive manner.
Advanced program management skills, with the demonstrated ability to successfully drive and land large and complex programs.
Bachelor's degree in a a technical field, or equivalent practical experience.
10 years of experience in program management.
5 years of experience leading hardware/software New Product Introduction (NPI) lifecycles for machine learning infrastructure (e.g., TPU, GPU, or similar AI-compute clusters).