Senior Manager Engineering – Meta Factory

Adobe Adobe · Enterprise · San Jose, CA

Senior Engineering Manager to build and lead teams owning Meta Factory, Adobe's Agentic Builders Experience. This platform helps agents understand goals, use tools, evaluate outcomes, and improve over time, serving as a company-wide agentic system. The role involves owning engineering execution for the agent harness, definitions, execution loop, evaluations, self-improvement, and skills framework.

What you'd actually do

  1. Own engineering execution for Meta Factory, including the agent harness, agent definitions, execution loop, evaluations, self-improvement systems, and skills framework.
  2. Lead development of the agent harness, including runtime, tool integrations, context management, control mechanisms, and execution environments.
  3. Build and lead a high-performing engineering team that partners cross-functionally to turn strategy into prioritized, reliable, secure, and scalable execution.
  4. Design feedback systems that help agents evaluate their work, learn from outcomes, and improve over time.
  5. Lead build-versus-buy evaluations across the Meta Factory stack, with clear recommendations on where to adopt external solutions and where Adobe should build key capabilities.

Skills

Required

  • Experience building and scaling engineering teams that deliver AI, developer platform, or infrastructure systems.
  • 5+ years of people management experience, including 2+ years leading teams that deploy AI applications in production.
  • Technical depth in AI/ML systems, including LLM APIs, agent frameworks, modern software engineering practices, and cloud platforms such as AWS or Azure.
  • Architecture judgment, including the ability to review designs, evaluate tradeoffs, identify fragile agent behavior, and catch production risks early.
  • Experience developing engineers, expanding team capabilities, and supporting career growth.
  • Clear communication skills, including the ability to explain technical concepts, align stakeholders, and drive decisions.

What the JD emphasized

  • deploy AI applications in production
  • evaluate tradeoffs, identify fragile agent behavior, and catch production risks early

Other signals

  • building and leading teams that deliver AI, developer platform, or infrastructure systems
  • deploying AI applications in production
  • LLM APIs, agent frameworks
  • evaluating tradeoffs, identify fragile agent behavior, and catch production risks early