Adobe
Software Engineer, Feedback & Learning Systems — Meta Factory
San Jose
Sponsorship not specified$139k-$258kDetected 8 days ago
PythonExpressAWSAzureCloud PlatformsRESTMachine LearningLLMsAgentic AIA/B Testing
About the role
- A platform that understands builders' intent, breaks down complex work, executes it through autonomous agents, and improves with every cycle.
Responsibilities
- Build feedback pipelines that capture agent outcomes, identify quality signals, and turn those signals into system improvements.
- Adobe empowers everyone to create through innovative platforms and tools that unleash creativity, productivity and personalized customer experiences.
Requirements
- 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.
Skills
- If these tools are used inappropriately or in a way that misrepresents your work, your application may not move forward in the process.
Compensation
- Our compensation reflects the cost of labor across several U.S. geographic markets, and we pay differently based on those defined markets.
- The U.S. pay range for this position is $139,000 -- $257,550 annually.
- Pay within this range varies by work location and may also depend on job-related knowledge, skills, and experience.
- Your recruiter can share more about the specific salary range for the job location during the hiring process.
- In California, the pay range for this position is $177,900 - $257,550
- At Adobe, for sales roles starting salaries are expressed as total target compensation (TTC = base + commission), and short-term incentives are in the form of sales commission plans.
Benefits
- Build Meta Factory's agent learning and feedback systems, including how agents evaluate outputs and improve over time.
- 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.
- 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.
- In addition, certain roles may be eligible for long-term incentives in the form of a new hire equity award.
This listing is sourced directly from Adobe's careers page and normalized into a canonical job model.