Technical Program Manager, Machine Learning Acceleration

Minimum qualifications:

• Bachelor's degree in a relevant technical or engineering field (e.g., Hardware, Computer Systems, Electrical Engineering, or Software Engineering), or equivalent practical experience in these domains.
• 8 years of experience in technical program management or a related engineering/operations role, leading the launch of cross-functional programs involving hardware/systems.
• Experience in machine learning infrastructure or program execution.

Preferred qualifications:

• 3 years of experience in two or more of these areas: data center design and construction, data center network deployment, ML machine deployment, and turnup.
• Substantial experience collaborating with and influencing stakeholders across multiple organizations and at all levels of leadership.
• Ability to self initiate with a firm sense of accountability and ownership for the end-to-end program lifecycle. Solid knowledge of the tools and elements of program management and leadership.
• Ability to comfortably shift between detailed execution and analysis and big picture thinking, prioritization, risk management, and communication.
• Excellent communication skills with significant experience presenting to executive-level audiences.

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.

As a Technical Program Manager focused on machine learning (ML) infrastructure deployment acceleration, you will play a pivotal role in managing ML fleets. You will be at the forefront of driving the infrastructure that enables path-breaking AI innovation and advancements. This role will require you to work with cross-functional teams across the company to orchestrate and ensure the rapid deployments of Google’s ML infrastructure globally and to enable delivery to all Google product areas in a timely fashion to meet their business needs. You will be responsible for key stakeholders and executive communications, and your work will directly impact the velocity and scalability goals of our global ML data center deployments.

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.

The US base salary range for this full-time position is $183,000-$271,000 + 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.

Responsibilities

• Orchestrate and drive the deployment of Google’s Tensor Processing Unit (TPU) and Graphics Processing Unit (GPU) Machine Learning (ML) platform deployments across global data centers.
• Ensure deployments land in timely fashion, meeting the needs of Google’s product areas.
• Drive best practices for velocity, fungibility, and scalability into our ML deployments to meet goals for ML acceleration over the next 12-24 months.
• Establish and lead a governance structure that facilitates effective working-level and executive alignment, communication, prioritization, risk mitigation, and decision making.
• Help launch new acceleration-related projects that expand our ability to drive global ML deployments.