Technical Program Manager III, AI/ML, Google Cloud

Minimum qualifications:

• Bachelor's degree in a technical field, or equivalent practical experience.
• 5 years of experience in program management.
• Experience in launching Machine Learning (ML) or Artificial Intelligence (AI) products from research to production.

Preferred qualifications:

• 5 years of experience managing cross-functional or cross-team projects.
• Experience with software infrastructure supporting the development of hardware/software machine learning architectures.

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.

Our goal is to build a Google that looks like the world around us — and we want Googlers to stay and grow when they join us. As part of our efforts to build a Google for everyone, we build diversity, equity, and inclusion into our work and we aim to cultivate a sense of belonging throughout the company.

As a Technical Program Manager, you will be responsible for delivering performance and cost projections for ML/AI initiatives, and your insights will support the hardware and software road map. You will also enable innovation across the stack by providing the software infrastructure needed to explore and evaluate new hardware/software architectures.

Responsibilities

• Drive programs to deliver definition, implementation and validation performance and cost metrics relevant for existing and future workloads and systems.
• Drive programs to identify future hardware and software opportunities for improved Machine learning (ML) performance and build, maintain and validate Hardware/Software simulators to enable evaluation of options and solutions (C++, Python).
• Track and manage the progress of various performance efforts identifying and mitigating risks, and ensuring that projects are completed on time and within budget. Communicate with stakeholders at all levels.
• Facilitate collaboration and coordination between the different teams involved in ML performance improvement.
• Collaborate with other teams (hardware, compiler, ML research) to improve the flow and results. Communicate results and turn them into improvements with cross-functional teams.