Technical Program Manager Ii, Kids and Families Core

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

Technical Program Manager II on the Kids and Families Core team at Google, responsible for scaling, optimizing, and deploying high-impact ML Models. This role involves managing the end-to-end model lifecycle from data pipeline engineering to validation, evaluation, and production deployment, with a focus on protecting millions of users worldwide. The TPM will partner with various cross-functional teams including ML engineers, researchers, data scientists, product managers, and legal experts.

What you'd actually do

  1. Partner with ML engineering, product, and data science teams to develop project plans, track milestones, identify technical risks, and drive programs to schedule.
  2. Coordinate and manage technical interdependencies across distinct workstreams (e.g., ensuring data engineering pipelines finish in time to feed model training and evaluation schedules).
  3. Partner with stakeholders across engineering, product, data science, and legal to navigate priorities, drive alignment, and ensure global regulatory compliance.
  4. Identify and implement operational process improvements to make ML model training, validation, and deployment cycles more efficient and scalable.
  5. Communicate program status, timelines, and technical risks clearly to the core team and downstream internal partner teams.

Skills

Required

  • program management
  • software development lifecycle (SDLC)
  • shipping technical products
  • managing software releases
  • deployment infrastructure
  • system testing
  • data pipelines
  • launch execution

Nice to have

  • managing cross-functional or cross-team projects
  • Machine Learning (ML) or Artificial Intelligence (AI) initiatives
  • model training
  • data pipeline engineering
  • evaluation
  • MLOps
  • ML infrastructure
  • model validation
  • user privacy
  • child safety
  • content moderation
  • regulatory compliance
  • translating complex technical concepts

What the JD emphasized

  • end-to-end model lifecycle
  • data pipeline engineering
  • validation
  • evaluation
  • production deployment
  • ML model training
  • validation
  • deployment cycles
  • global regulatory compliance

Other signals

  • ML Models
  • model lifecycle
  • production deployment
  • ML model training, validation, and deployment cycles