Senior ML/AI Engineering Manager, GenAI Experiences

Our GenAI Experiences Engineering team delivers customer-facing AI capabilities across Adobe Experience Cloud. We operate with a startup mentality: rapid delivery of value, iterative learning, and scalable solutions that hold up in production.

As GenAI Experiences mature, we are expanding beyond reactive chat interactions into proactive, grounded, workflow-aware intelligence across Experience Cloud. This role leads the ML/AI squad responsible for building the ML foundations and systems that make that shift possible.

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 ML/AI leadership 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 required - consistent with M50 expectations.

You will partner closely with Product, Design, Application Engineering, and Agent/Orchestration counterparts to ensure ML systems are shippable, measurable, privacy-conscious, and latency-aware, and that they translate into durable product experiences rather than research prototypes.

Impact You'll Make

In this role, you will directly influence Adobe's ability to deliver differentiated GenAI experiences at scale by building the ML systems that unlock:

• Real-Time Contextual Intelligence - instant page-level insights on the edge

• Predictive User Workflow Engine - learning and predicting intent/next steps/insights over time and accounting for the broader multi-application context

• Natural Language Chat-Based UI Control - turning intent into safe, grounded agents to connect users to cross-application functionality

You will also drive outcomes tied to the roadmap's experience metrics (examples include engagement with surfaced insights, proactive recommendations, and defining specific benchmarks that align with both ML/AI quality +user experience quality).

At this level, expectation is high leverage: you shape outcomes across multiple roadmaps and teams by establishing the ML operating model, system ownership, and delivery rigor - not just optimizing within one model or one feature area.

What You'll Own (ML Workstreams)

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

Key Responsibilities

• Lead the ML/AI squad: hire, mentor, and retain senior ML engineers; build a culture of applied rigor, startup style fast iteration, and measurable impact.

• Set strategy and one-year objectives for the Experience ML program, translating multi-year vision into deliverable release plans and clear priorities aligned to product outcomes

• Own end-to-end execution for ML systems powering GenAI Experiences across the three themes (real-time contextual intelligence, predictive workflows, NL UI control).

• Operationalize ML Execution and Quality Standards: establish standards for data readiness, evaluation, model quality, latency, privacy constraints, monitoring, and iteration loops so results reliably ship and improve.

• Drive cross-team tradeoffs and dependency management between ML workstreams and System Experience surfaces and UI Experience surfaces (where models must integrate into your peer teams).

• 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 (latency vs. quality, privacy vs. telemetry, offline vs. online inference).

• Communicate with senior leadership (Sr. Director/VP partners) to drive alignment on workstreams, resourcing approach, and strategic tradeoffs

Qualifications

• 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.

• 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.

• Strong ML/AI/NL background sufficient to lead technical strategy and execution across: real-time contextual modeling, workflow/behavior modeling, and grounded NL-to-action systems.

• Proven ability to lead ambiguous, cross-team initiatives, balancing speed, quality, privacy, and long-term sustainability.

• Excellent communication and influence skills across technical and non-technical partners, including executive alignment on tradeoffs and investment.

Nice to Have

• 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

• Bachelor's , Master's, PhD in Machine Learning, Artificial Intelligence, Computer Science, Engineering, or related field