Technical Program Manager III, Resource Management, Google Ads

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.

As part of Commerce Resource Management, you will be working in a dynamic environment, balancing the allocation of finite data center resources to best fit business needs. You will be a primary point-of-contact for forecasting, planning, and allocation of capacity needs across Commerce to ensure that proper financial accountability yields successful launches and growth for our products. This team is focused on driving efficiencies to ensure resources are used effectively, and planning toil is minimized.

Google Ads is at the forefront of AI innovation, applying cutting-edge machine learning and Generative AI models like Gemini to power a multi-billion dollar global business.

Our work directly impacts billions of users by protecting users from harm, improving ad quality, and optimizing campaigns for advertiser return-on-investment. We foster a culture of deep collaboration, partnering closely with teams like Google Research and DeepMind to solve complex challenges. Join us to work on state-of-the-art AI, take on problems at an unparalleled scale, and build the next generation of advertising technology.

Individual pay is determined by factors including job-related skills, experience, and relevant education or training.

US: $163000 - $236000 (USD) + 15% bonus target + equity + benefits

Learn more about benefits at Google.

• Own the plan of record for the commerce product area's infrastructure resource needs, serving as the primary subject matter expert and consultant on technical and financial trade-offs in machine resource management.
• Partner with Engineering, Product Management, and SRE to translate business growth plans into concrete resource requirements, while actively influencing engineering teams to invest in resource efficiency and control growth.
• Aggregate and communicate complex resource needs and tradeoffs (such as cost versus service reliability) to secure necessary resources, and represent the commerce business in cross-product area discussions and negotiations.
• Drive regular communication cadences to ensure all teams are aligned on both short- and long-term plans, while owning and maintaining stakeholder-facing policies and service level objectives.
• Build tooling and leverage artificial intelligence to automate and simplify resource management processes, thereby improving operational accuracy and reducing toil.

Minimum qualifications:

• Bachelor's degree in a technical field, or equivalent practical experience.
  
• 5 years of experience in program management.
  
• Experience working with historical datasets utilizing programming languages such as SQL and Python.
• Experience with planning or forecasting for data center infrastructure or supply chain (e.g., resource management, capacity planning, inventory planning, and demand forecasting).

Preferred qualifications:

• 5 years of experience managing cross-functional or cross-team projects.
• Relevant experience in PARM (Product Area Resource Management) or infrastructure planning roles.
• Domain expertise in data center infrastructure (compute, storage, networking) or Machine Learning (ML) infrastructure, specifically TPU/GPU.