Senior Ml/ai Engineering Manager, Genai Experiences

Adobe Adobe · Enterprise · San Jose, CA +1

Senior ML/AI Engineering Manager to lead the Experience ML/AI squad within GenAI Experiences. The role owns the ML strategy for AI Assistant, moving it towards real-time contextual intelligence, predictive workflow understanding, and natural-language UI control across Experience Cloud. Requires setting strategy, operationalizing ML execution, and scaling impact. Focuses on user outcomes and building proactive, grounded intelligence using UI context and user behavior signals.

What you'd actually do

  1. Set strategy and one-year objectives for the Experience ML program
  2. Operationalize ML Execution and Quality Standards
  3. Scale impact by both with and without headcount-only scaling (improving operating models, tooling, automation, and reuse across roadmaps and workstreams).
  4. Remain hands-on enough to unblock: review strategic PRs/designs, guide technical choices, and help the team navigate tradeoffs
  5. Communicate with senior leadership (Sr. Director/VP partners) to drive alignment on workstreams, resourcing approach, and strategic tradeoffs

Skills

Required

  • 10+ years of engineering experience with strong technical depth
  • 6+ years of engineering management experience
  • Experience building ML systems that modeling user behavior/workflows/data
  • Excellent communication and influence skills

Nice to have

  • Master’s degree or PhD
  • Experience with on-device / edge ML constraints
  • Experience with knowledge graphs / heterogeneous modeling approaches
  • Experience building human-in-the-loop systems
  • Experience building UI and user experience workflows

What the JD emphasized

  • operates beyond a single project or model
  • shipping production systems
  • engineering management experience
  • modeling user behavior/workflows/data
  • on-device / edge ML constraints
  • knowledge graphs / heterogeneous modeling
  • building human-in-the-loop systems

Other signals

  • leading ML strategy for AI Assistant
  • moving from reactive agents to real-time contextual intelligence
  • predictive workflow understanding
  • natural-language UI control across Experience Cloud
  • setting strategy and one-year objectives
  • operationalizing ML execution and quality standards
  • scaling impact by improving operating models, tooling, automation, and reuse