Manager, Applied Science

Adobe Adobe · Enterprise · San Jose, CA

Manager for a team building core systems that support training and development of foundation models for generative AI (image, video, multimodal). Focus on foundational training frameworks, tooling, and codebases for large-scale experimentation, reliable execution, and rapid iteration. Requires strong software engineering, systems experience, and applied ML understanding.

What you'd actually do

  1. Lead and grow a team of applied scientists and engineers working on core generative AI training systems
  2. Own and evolve key parts of the training and experimentation codebase, focusing on reliability, scalability, and developer productivity
  3. Partner closely with applied research and engineering teams to support the full lifecycle from experimentation to production
  4. Drive technical design and architecture decisions for large-scale training systems, pipelines, and supporting infrastructure
  5. Establish best practices for system robustness, testing, observability, and reproducibility

Skills

Required

  • Master’s or PhD in Computer Science, Engineering, AI/ML, or a related technical field, or equivalent practical experience
  • At least 2+ years of experience leading or managing technical teams in large-scale systems engineering or AI/ML domains
  • Strong hands-on software engineering experience building and operating large-scale distributed systems, or experience working on model training systems and ML infrastructure
  • Experience working with complex codebases, production services, or internal platforms used by multiple teams
  • Ability to operate as a technical leader, making sound design tradeoffs and unblocking complex engineering problems
  • Strong communication skills and the ability to collaborate across research, engineering, and product organizations
  • Comfort working in fast-moving, ambiguous environments typical of generative AI development

What the JD emphasized

  • core systems that support training and development of foundation models
  • foundational training frameworks, tooling, and codebases
  • foundational capabilities and engineering systems
  • large-scale training systems, pipelines, and supporting infrastructure
  • system robustness, testing, observability, and reproducibility

Other signals

  • foundation models
  • training frameworks
  • large-scale experimentation
  • scalable systems