Technical Program Manager

Google Google · Big Tech · Mountain View, CA +1

Technical Program Manager at Google DeepMind focused on managing the program lifecycle for AI/ML systems, infrastructure, and product evaluations. The role involves monitoring evaluation processes, defining performance metrics, developing data pipelines, and collaborating with research and engineering teams to drive progress and ensure program success. Requires experience in program management for complex technical products, technical judgment in ML systems, and knowledge of GenAI/LLM systems and evaluations.

What you'd actually do

  1. Monitor critical evaluation processes and communicate status across the organization
  2. Define and automate key performance metrics to track evaluation success
  3. Develop data pipelines and reporting dashboards to provide program visibility
  4. Collaborate with research and engineering partners to align on priorities and drive progress
  5. Manage the full program lifecycle from scope definition to execution and governance. Apply technical judgment to improve evaluation infrastructure and solve complex problems.

Skills

Required

  • Bachelor’s degree in Computer Science, Computer Engineering, Artificial Intelligence, Machine Learning or a related field
  • 2 years of experience in the job offered or in a Technical Program Manager-related occupation
  • Planning and program management for complex technical products across the full product lifecycle from prototype to end-of-life or product development lifecycle management
  • Applying technical judgment to assess engineering problems and shape technical directions in machine learning systems or technical problem assessment
  • Knowledge of machine learning, GenAI, and LLM systems, infrastructure, and product evaluations
  • Stakeholder management with engineering and product management teams to deliver technical products or cross-functional team alignment
  • Managing organizational change in a rapidly moving environment using rollout plans and impact analysis or change implementation

What the JD emphasized

  • Knowledge of machine learning, GenAI, and LLM systems, infrastructure, and product evaluations
  • Applying technical judgment to assess engineering problems and shape technical directions in machine learning systems or technical problem assessment
  • Monitor critical evaluation processes
  • Define and automate key performance metrics to track evaluation success