Minimum qualifications:- Bachelor's degree in Computer Science, Engineering, Data Science, related technical field, or equivalent practical experience.
- 5 years of experience leading engineering projects across multiple geographies and time zones.
Preferred qualifications:- Experience in people management.
- Experience in Large Language Model (LLM) evals, model training, model releases, or data science.
- Ability to quickly learn and deeply understand the technical aspects of the programs from interface to infrastructure, serving, and customer issues, and drive technical discussions.
- Excellent skills in managing complex stakeholder relationships.
About the jobGoogle'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.
Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
US: $217000 - $237000 (USD) 15% bonus target equity benefits
Learn more about benefits at Google .
Responsibilities - Collaborate closely with the engineering team to support infrastructure development, meticulously track priorities, issues, deeply understand evaluation and experimentation results; coordinate alignment and, assessment with model teams.
- Collaborate with data scientists to design and integrate model evals, execute evals, and conduct loss analysis to inform model quality.
- Manage Gemini autorater platform and Gemini eval area compute capacity planning. Provide proactive status updates to stakeholders, and effectively triage autorater issues back to the modeling team.
- Drive strategic goals and tactical delivery. Lead with high agency. Proactively identify workflow gaps and implement scalable improvements to optimize efficiency and output quality.
