About The Position

Changing the world through digital experiences is what Adobe’s all about. We give everyone—from emerging artists to global brands—everything they need to design and deliver exceptional digital experiences! We’re passionate about empowering people to create beautiful and powerful images, videos, and apps, and transform how companies interact with customers across every screen. We’re on a mission to hire the very best and are committed to creating exceptional employee experiences where everyone is respected and has access to equal opportunity. We realize that new ideas can come from everywhere in the organization, and we know the next big idea could be yours! The Opportunity We are seeking an experienced Senior ML/AI Engineering Manager (M50) to lead the Experience ML/AI squad within GenAI Experiences. This role owns delivery of the ML strategy that moves AI Assistant from a reactive experience (driven primarily by agents) to real-time contextual intelligence, predictive workflow understanding, and safe natural-language UI control across Experience Cloud. This is a uniquely Experience focused role . We start from user outcomes—how products feel, work, and deliver value—and back into the right mix of ML techniques using UI context and user behavior/workflow signals to build proactive, grounded intelligence across Experience Cloud This is intentionally scoped for a senior leader who operates beyond a single project or model. Success requires setting strategy and one-year objectives , delegating across direct reports, and making complex tradeoffs where in-depth knowledge of organizational goals and product constraints is — consistent with M50 expectations. You will lead execution across six persistent ML workstreams (the organizational unit of delivery), ensuring clear ownership, cross-team dependencies, and reusable ML foundations: Page & UI Semantic Understanding and Manipulation User Behavior, Intent & Workflow Analytics Heterogeneous Object & Graph Modeling NL Interaction with Cross Product New Services via Agents Edge Inference & Model Optimization Human-in-the-Loop Control & Learning

Requirements

  • A Bachelor’s degree or equivalent experience in a relevant technical or scientific field such as Computer Science, Information Science, Human-Computer Interaction, Artificial Intelligence, Machine Learning, Statistics, Mathematics, Physics, Cognitive Science, or Psychology.
  • Master’s degree or PhD a plus , particularly in areas related to machine learning, human-centered AI, applied statistics, or computational sciences, but not .
  • 10+ years of engineering experience with strong technical depth; experience shipping production systems that require high reliability and strong operational rigor.
  • 6+ years of engineering management experience, including leading senior engineers and/or managers; ability to set direction and delegate across complex domains consistent with high visibility scope.
  • Experience building ML systems that modeling user behavior/workflows/data ( e.g. event streams, interfaces as data, page semantics ) and to infer intent and drive proactive UX — beyond traditional “system metrics/logs only” ML.
  • Excellent communication and influence skills across technical and non-technical partners, including executive alignment on tradeoffs and investment.

Nice To Haves

  • Experience with on-device / edge ML constraints (latency, memory, privacy), and/or model optimization for client environments (the roadmap emphasizes client-side models and “edge inference & optimization”).
  • Experience with knowledge graphs / heterogeneous modeling approaches for cross-product grounding and reuse.
  • Experience building human-in-the-loop systems (annotation, feedback, calibration, explainability, interruption management).
  • Experience building UI and user experience workflows

Responsibilities

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