Senior Technical Program Manager I, AI Data Full-time

Minimum qualifications:

• Bachelor’s degree in a technical field, or equivalent practical experience.
• 8 years of experience in program management.
• Experience in Machine Learning or Artificial Intelligence.
• Experience with data analysis or data analytics.

Preferred qualifications:

• 8 years of experience managing cross-functional or cross-team projects.
• Experience with quantitative analysis and cost-effectiveness assessment techniques and data quality metrics for AI products.
• Experience in project and workstream management and developing customer relationships.
• Experience in systems integration, data transfer/management, or enterprise database performance.
• Understanding of AI/ML related infrastructure technologies (e.g., GPUs, TPUs, LLMs, foundational models) and use cases (e.g., training, inference, tuning etc).
• Excellent verbal and written communication skills, with the ability to communicate clearly and effectively with multiple and diverse stakeholders across businesses, leadership, and teams.

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.

The Machine Learning (ML) Data Infrastructure teams, which are part of the ML data organization, strive to attain data life-cycle excellence for ML by providing fundamental components for various data issues in ML. As larger models become increasingly crucial to Google’s success, the development of ML is becoming more data-centric.

In this role, you will collaborate with cross-functional teams and product leadership to harness data and data systems to accelerate the development of extensive Machine Learning (ML) models.

Google Cloud accelerates every organization’s ability to digitally transform its business and industry. We deliver enterprise-grade solutions that leverage Google’s cutting-edge technology, and tools that help developers build more sustainably. Customers in more than 200 countries and territories turn to Google Cloud as their trusted partner to enable growth and solve their most critical business problems.

The US base salary range for this full-time position is $168,000-$252,000 bonus equity benefits. Our salary ranges are determined by role, level, and location. The range displayed on each job posting reflects the minimum and maximum target salaries for the position across all US locations. 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.

Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits. Learn more about benefits at Google .

Responsibilities

• Implement communications standards across a portfolio of programs including executive and key partner communications.
• Establish a reliable and visible cadence for program reviews, decision-making, prioritization, and resource stewardship (effective deployment of machine and people resources) whereby improvements such as efficiency and utilization gains are measurable and the impact can be felt organization wide.
• Lead a governance structure that drives effective executive decision-making. Ensure governance structure effectively exposes and mitigates dependencies.
• Identify change management opportunities that increase program velocity and which affect multiple teams.
• Define/manage a program portfolio solving problems that aim high business impact for the organization and product area.