Director of Engineering – Machine Learning & AI Products

Adobe Adobe · Enterprise · San Jose, CA

Director of Engineering role focused on leading the development and end-to-end delivery of AI-native, production-ready machine learning products for external clients. This involves managing a growing ML engineering organization, driving technical and product leadership, and ensuring operational success of customer-facing AI systems.

What you'd actually do

  1. Own the end‑to‑end delivery of AI/ML solutions deployed in production, moving beyond proofs‑of‑concept to scalable, reliable, customer‑facing systems.
  2. Establish, grow, and manage a high-caliber ML engineering organization, scaling the team from about 4 to over 20 engineers as time progresses.
  3. Act as a senior technical voice with customers, handling partner concerns, roadmap discussions, and production issue resolution.
  4. Ensure excellence in production operations, including monitoring, incident response, model performance, and customer‑reported issues.
  5. Collaborate directly with Product Management to build product vision, challenge assumptions, and ensure differentiation beyond general‑purpose LLM capabilities.

Skills

Required

  • Over 15 years of experience in software engineering
  • extensive expertise in machine learning or systems driven by artificial intelligence
  • Demonstrated history of delivering and managing AI/ML products in production
  • Strong business and commercial exposure
  • direct ownership or leadership of a customer-facing, revenue-impacting AI/ML product
  • Strong experience with ML system build, production architecture, and real‑world operational challenges
  • Demonstrated success leading senior technical teams
  • Ability to engage deeply with product partners
  • Experience working directly with external customers

Nice to have

  • Proven track record of building AI/ML products in startup or startup-like contexts
  • Background in external, customer‑facing platforms or products, particularly AI‑first offerings
  • Familiarity with domains such as marketing technology, sales technology, or customer experience platforms

What the JD emphasized

  • production-ready machine learning products
  • entire lifecycle of sophisticated AI systems
  • customer engagement, and continuous operational success
  • production, customer engagement, and continuous operational success
  • end‑to‑end delivery of AI/ML solutions deployed in production
  • scalable, reliable, customer‑facing systems
  • delivering and managing AI/ML products in production
  • customer-facing, revenue-impacting AI/ML product
  • ML system build, production architecture, and real‑world operational challenges
  • production operations
  • customer-reported issues
  • post‑launch support

Other signals

  • delivering and managing AI/ML products in production
  • customer-facing, revenue-impacting AI/ML product
  • ML system build, production architecture, and real-world operational challenges
  • scaling the team from about 4 to over 20 engineers