Senior Staff Technical Program Manager, AI Innovation and Research

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

Technical Program Manager to drive strategy and execution for AI agent initiatives transforming the software development lifecycle. This role involves leading complex, cross-functional programs at the intersection of AI and product, focusing on democratizing app creation and building a virtual software engineering workforce. Requires navigating ambiguity from 0-to-1 product phases and partnering with various Google teams to bring self-sustaining agents to life.

What you'd actually do

  1. Lead the strategy, planning, and execution for the software engineer agents pillar. Direct the operational cadence for high-impact initiatives including project spark (e.g., democratized app creation) and virtual software engineers for self-sustaining workflows.
  2. Drive the cross-functional delivery of high-profile initiatives enabling non-technical users to generate, iterate, and share apps and dynamic web experiences using natural language.
  3. Operationalize self-sustaining workforce pilots across key Platforms and Devices (P&D) partner teams (Android, Fuchsia, Chrome) to manage the integrations and issue trackers, revolutionizing engineering velocity.
  4. Partner with trust and safety, legal, and google play to define the distribution models and defense-in-depth safety strategies for AI-generated apps, ensuring strict compliance with Android ecosystem standards.
  5. Bridge foundational models with product applications. Navigate complex engineering challenges integrating LLM-based coding agents with existing developer infrastructure (e.g., cloud run sandboxing, omnilab, play developer APIs).

Skills

Required

  • technical program management
  • cross-functional leadership
  • strategic planning
  • project execution
  • stakeholder management
  • risk management
  • technical expertise

Nice to have

  • delivering complex, multi-year AI/ML systems
  • Large Language Models (LLMs)
  • Generative AI
  • self-sustaining agent frameworks
  • large-scale cloud or ML compute infrastructure
  • complex resource allocation/capacity planning
  • trust and safety
  • policy compliance
  • platform distribution policies for consumer-facing AI and mobile products
  • Software Development Lifecycle (SDLC)
  • developer tools (IDEs, Buganizer, Gerrit)
  • mobile application architectures (Android)
  • multi-agent systems (RAG, Model Context Protocol (MCP))

What the JD emphasized

  • extreme ambiguity from 0-to-1 product phases
  • complex engineering challenges integrating LLM-based coding agents with existing developer infrastructure

Other signals

  • AI agents
  • multi-agent systems
  • LLM-based coding agents
  • democratizing app creation
  • virtual software engineering workforce