Engineering Manager, Express AI Foundations

Adobe Adobe · Enterprise · San Jose, CA

Engineering Manager to lead a team building the AI infrastructure for Adobe Express, focusing on Agentic AI, Imaging AI, Motion AI, and Personalization AI. The role involves technical leadership, people management, and owning the delivery of the AI stack including LLM orchestration, inference services, data pipelines, and evaluation frameworks.

What you'd actually do

  1. Own end-to-end delivery of AI infrastructure workstreams — including LLM orchestration, inference services, data pipelines, and evaluation frameworks — from planning through production.
  2. Partner with product management & engineering leadership to decompose high-level product requirements into concrete technical requirements — breaking ambiguous asks into scoped workstreams with clear dependencies, effort estimates, and sequencing.
  3. Manage and grow a team of 6–10 engineers across varying seniority levels, providing regular coaching, feedback, and career development support.

Skills

Required

  • Engineering management experience
  • delivering complex infrastructure or platform projects
  • distributed systems
  • AI/ML infrastructure
  • large-scale service development
  • owning team execution end-to-end
  • structured prioritization
  • dependency management
  • shipping reliably
  • agile, fast-moving environment
  • clear, structured communication skills
  • translating technical trade-offs into business terms
  • influencing direction without authority
  • navigating ambiguity
  • defining scope
  • making decisions with incomplete information
  • adapting plans quickly
  • working fluency in modern AI/ML concepts
  • LLM orchestration
  • inference infrastructure
  • prompt engineering
  • AI output evaluation
  • data pipelines
  • growing engineers
  • coaching
  • setting expectations
  • giving actionable feedback
  • supporting career progression

Nice to have

  • Masters degree or equivalent experience in Computer Science, Machine Learning, or a related field
  • Background as a hands-on engineer in data infrastructure, ML platform, or large-scale backend systems
  • Experience hiring and ramping engineers across a range of seniority levels
  • Exposure to Generative AI development
  • LLMs
  • diffusion models
  • multimodal systems
  • Familiarity with MLOps practices
  • feature stores
  • model registries
  • evaluation pipelines
  • deployment workflows
  • Actively tracks emerging AI/ML trends
  • Awareness of security, data privacy, and responsible AI concerns specific to AI-backed systems
  • bias
  • safety
  • handling of user data in model pipelines

What the JD emphasized

  • AI infrastructure
  • LLM orchestration
  • inference services
  • evaluation frameworks
  • Agentic AI
  • Imaging AI
  • Motion AI
  • Personalization AI
  • responsible AI practices
  • bias awareness
  • data privacy for AI systems
  • technical debt versus feature velocity
  • structured prioritization
  • dependency management
  • shipping reliably
  • Generative AI development
  • MLOps practices
  • security
  • data privacy
  • responsible AI concerns

Other signals

  • AI infrastructure
  • LLM orchestration
  • inference services
  • evaluation frameworks
  • Agentic AI
  • Imaging AI
  • Motion AI
  • Personalization AI