> Markdown version of [/jobs/ext/1265976-software-engineer-feedback-learning-systems-meta-factory](https://www.wearedevelopers.com/jobs/ext/1265976-software-engineer-feedback-learning-systems-meta-factory). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

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# Software Engineer, Feedback & Learning Systems - Meta Factory

- **Company:** Adobe Systems
- **Location:** San Jose, CA, United States
- **Experience:** Expert
- **Salary:** $177,900.0 - $257,550.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Amazon Web Services, Microsoft Azure, Code Review, Learning Management Systems, Python (Programming Language), Software Engineering, Reinforcement Learning, Large Language Models, Adobe, Data Pipelines, Programming Languages
- **Published:** July 14, 2026
- **Apply:** https://adobe.wd5.myworkdayjobs.com/external_experienced/job/San-Jose/Software-Engineer--Feedback---Learning-Systems---Meta-Factory_R170225-1

## About the Role

* 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.

About Adobe

## Description

* 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.


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