Software Engineer, Feedback & Learning Systems — Meta Factory

Adobe Adobe · Enterprise · San Jose, CA

Software Engineer role focused on building the core agentic platform (Meta Factory) at Adobe, which includes agent execution, tool use, feedback loops, and evaluation systems for thousands of internal engineers. The role involves applying AI techniques like preference learning and RLHF to improve agent quality and defining the learning layer's integration with other system components.

What you'd actually do

  1. Build Meta Factory’s agent learning and feedback systems, including how agents evaluate outputs and improve over time.
  2. Build feedback pipelines that capture agent outcomes, identify quality signals, and turn those signals into system improvements.
  3. Apply AI-first techniques such as preference learning, reward modeling, reinforcement learning, and evaluation-driven improvement to raise agent quality re`lease over release.
  4. Define how the learning layer connects with the Agent Harness, evaluation infrastructure, skills layer, and execution loop.
  5. Write clear development docs, review code, and mentor engineers inventing AI systems that learn from user input.

Skills

Required

  • Python
  • agent execution
  • tool calling
  • context and state management
  • sandboxed runtime systems
  • LLM and agentic systems
  • preference learning
  • reward modeling
  • reinforcement learning
  • evaluation-driven improvement
  • cloud platforms (AWS or Azure)
  • data pipeline tools
  • ML experimentation infrastructure

Nice to have

  • delivery of complex AI, ML, or data-intensive systems
  • RLHF
  • preference learning
  • reward modeling
  • AI techniques to real products or platforms with measurable impact
  • other programming language

What the JD emphasized

  • delivery of complex AI, ML, or data-intensive systems
  • Hands-on experience with LLM and agentic systems
  • tool use
  • output evaluation
  • feedback-driven improvement techniques
  • AI techniques to real products or platforms with measurable impact

Other signals

  • agent execution
  • tool calling
  • feedback systems
  • evaluation infrastructure