Senior Technical Program Manager II, AI/ML

Google•Mountain View, CA

9d•$240,000 - $333,000

About The Position

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. As a Senior Technical Program Manager in Labs/GDM-SRE, you will drive operational excellence at the forefront of AI innovation. You will lead launch coordination, reliability standards, and ML/compute capacity procurement for fast-growing Labs products in close partnership with senior stakeholders, while helping to scale the GDM SRE 'Launch Control' model support rotation across DeepMind to ensure seamless, reliable releases at scale. 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 [https://www.google.com/about/careers/applications/benefits/].

Requirements

Bachelor's degree in a technical field, or equivalent practical experience.
10 years of experience in program management.
Experience with artificial intelligence/machine learning projects, tools, or infrastructure.
Experience with capacity management in a cloud environment.
Experience in Technical Program Management, Partner Management, and Release and Launch Readiness.

Nice To Haves

Master's degree in Computer Science or a related technical field.
10 years of experience managing complex cross-functional or cross-team projects.
Experience in a Site Reliability Engineering role.
Ability to quickly learn and understand the technical context of programs you drive and to contribute to technical discussions that affect the programs.
Excellent organizational, investigative, prioritization, communication and influencing skills. Ability to manage multiple concurrent complex interactions with geographically disparate teams.

Responsibilities

Lead cross-functional launch readiness for Labs and GDM dependencies. Standardize release playbooks, traffic ramp strategies, and bridge product, research, and SRE teams to seamlessly transition experimental prototypes into Google-scale products.
Partner with engineering to enforce operational requirements like Service Level Objectives (SLOs) and failover architectures. Anticipate failure modes through readiness drills, rollback strategies, and post-launch reviews.
Manage the capacity life-cycle for machine learning (ML) accelerators and traditional compute across both rapidly scaling models and early-stage experiments.
Collaborate with SRE and Product engineers to eliminate operational bottlenecks by building tooling for quota tracking, launch management, and readiness verification.
Provide senior leadership with data-driven launch updates, capacity runway forecasts, risk assessments, and clear escalation pathways.