Software Engineer, Feedback & Learning Systems — Meta Factory
About The Position
Adobe Unified Platform is building the system that changes how software gets made at Adobe. A platform that understands builders’ intent, breaks down complex work, executes it through autonomous agents, and improves with every cycle. What this team ships multiplies across thousands of Adobe engineers, and it's the substrate the rest of Adobe's agentic efforts build on. Meta Factory is the core of that platform: the Agentic Builders Experience that defines how agents understand their goals, decompose work, use tools, evaluate their own outcomes, and improve over time. Think of it as our own version of Claude Code, optimized for Adobe needs, model independent and operated for the whole company. We are looking for a Software Engineer with strong hands-on expertise in agent execution, tool calling, context and state management, and sandboxed runtime systems to build and own key components of Meta Factory's Agent Harness.
Requirements
- 8+ years of software engineering experience, including delivery of complex AI, ML, or data-intensive systems.
- Hands-on experience with LLM and agentic systems, including tool use, context management, output evaluation, and feedback-driven improvement techniques such as RLHF, preference learning, or reward modeling.
- Ability to make progress in ambiguous technical areas and turn broad goals into working systems.
- Experience applying AI techniques to real products or platforms with measurable impact.
- Proficiency in Python and at least one other programming language.
- Experience with cloud platforms such as AWS or Azure, data pipeline tools, and ML experimentation infrastructure.
Responsibilities
- Build Meta Factory’s agent learning and feedback systems, including how agents evaluate outputs and improve over time.
- Build feedback pipelines that capture agent outcomes, identify quality signals, and turn those signals into system improvements.
- Apply AI-first techniques such as preference learning, reward modeling, reinforcement learning, and evaluation-driven improvement to raise agent quality re`lease over release.
- Define how the learning layer connects with the Agent Harness, evaluation infrastructure, skills layer, and execution loop.
- Write clear development docs, review code, and mentor engineers inventing AI systems that learn from user input.
Benefits
- Comprehensive benefits programs
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What This Job Offers
Job Type
Full-time
Career Level
Senior
Education Level
No Education Listed
