Senior Technical Program Manager, AI Data, Google Cloud

Minimum qualifications:

• Bachelor's degree or equivalent practical experience.
• 8 years of experience in program management.
  
• 8 years of experience working with machine learning.
  
• 2 years of experience in data analysis.
  

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 technical program management in AI or data-centric projects.
• Excellent problem-solving skills, with the ability to navigate scenarios.
• Excellent communication and leadership skills, with the ability to lead cross-functional teams and convey project status to diverse audiences.
  

About the job

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.

The team has been working on the critical paths for recent and upcoming LLM launches at Google, including Bard, Gemini, Magi, and Cloud. In this role, you will collaborate with cross-functional teams and product leadership to utilize data and systems to accelerate the development of extensive ML models. You will also work to provide easy access to high-quality data for any AI task, and secure AI models with tamper-proof lineage, transparent approvals, and secured storage for accelerated, accountable product and model launches. You will also evaluate, track, remediate, and prevent the recurrence of safety and fairness risks.

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

• Lead programs for tamper-proof lineage, transparent approvals, and secured storage.
  
• Work with engineering, product management, and other partners to define all work-streams, requirements, schedules, resources, and milestones.
  
• Drive feature planning and build cohesion and alignment across stakeholders.
  
• Oversee important customer engagements, promote new evaluation methodologies and best practices, and identify KPIs and data quality metrics for success.
  
• Lead programs in evaluation of safety risks and data acquisition.