Sr. Manager, Machine Learning

Adobe Adobe · Enterprise · San Francisco, CA

Lead a team of ML engineers building the model, agent, and inference systems for a new AI-first web product in the 'vibe coding' category. This role involves setting technical direction, driving architectural decisions for agentic and model-serving systems, and ensuring production excellence for the ML stack. The focus is on shipping AI-powered features in customer-facing web products.

What you'd actually do

  1. Lead a team of machine learning engineers building the model, agent, and inference systems that power a new AI-first web product — set the bar, the operating rhythm, and the culture.
  2. Partner closely with product management, design, and applied research to translate product intent into a credible ML roadmap; balance speed and quality to ship iteratively to real users.
  3. Drive the right architectural calls for an agent-native product — model and tool routing, latency and cost, evaluation and telemetry, safety and trust — in partnership with the broader Adobe AI platform.
  4. Own production excellence for the ML stack: reliability, performance, observability, and a tight loop from field signal back into evaluation.
  5. Play a pivotal role in attracting, hiring, onboarding, and retaining exceptional engineers through mentorship, and grow them professionally and personally.

Skills

Required

  • BS or MS in Computer Science or equivalent experience.
  • 10+ years of experience in software / ML engineering, with a demonstrated understanding of software development principles, practices, and methodologies.
  • 3+ years leading engineering teams, including managing senior ICs; a track record of hiring and growing high-performing teams in the US market.
  • Strong technical depth in ML engineering — at least one of: modern LLM / agent application stacks, model serving and inference optimization, retrieval and tool-use systems, or evaluation infrastructure for AI products.
  • Background shipping AI-powered features in customer-facing web products; comfortable reasoning about both the model side and the application side of the stack.
  • Proven ability to drive consensus on requirements, ensure timely decision-making, provide guidance, prioritize tasks, and optimize scope and time.
  • Excellent social and communication skills; able to effectively communicate ideas and influence others across product, design, research, and engineering — including across geographies and timezones.

Nice to have

  • prior experience with creative tools, design systems, code generation, or developer-facing AI products.

What the JD emphasized

  • Lead a team of machine learning engineers
  • senior ML engineering team
  • agent-native product
  • model serving and inference optimization
  • retrieval and tool-use systems
  • evaluation infrastructure for AI products
  • shipping AI-powered features in customer-facing web products

Other signals

  • AI-first web application
  • AI-native creative tools
  • agent-native product