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# Software Engineering AI Trainer

- **Company:** LinkedIn Corporation
- **Location:** United States (Remote available)
- **Experience:** Experienced
- **Salary:** $208,000.0
- **Contract:** Permanent contract
- **Skills:** Java (Programming Language), JavaScript (Programming Language), Artificial Intelligence, Software Bug Management, C Sharp (Programming Language), Code Coverage, Code Review, Software Debugging, Junit, Python (Programming Language), Software Engineering, TypeScript, Prompt Engineering, Backend, Xunit, Pytest, Integration Tests, Machine Learning Operations, Evaluation of Large Language Models, Code Restructuring, Golang
- **Published:** October 14, 2026
- **Apply:** https://www.juju.com/job/2_163942635

## About the Role

* Advanced professional written proficiency in English
 * 3-5 years of professional software engineering experience
 * Strong proficiency in Python and JavaScript/TypeScript; xrbllit working knowledge of Java, C#, or Go
 * Backend or full-stack development experience in production systems
 * Experience with testing frameworks (e.g., pytest, Jest, JUnit, xUnit, Go testing)
 * Proven ability to debug and navigate large, multi-file codebases
 * Experience with code reviews, refactoring, and production migrations
 * Prior experience with data annotation, evaluation, or structured labeling for AI/ML systems
 * Experience defining solution trajectories and writing evaluation rubrics
 * Familiarity with LLM evaluation, prompt design, or benchmarking workflows

## Description

Contributors will design and evaluate realistic software engineering tasks, including bug resolution, feature implementation, refactoring/migration, and test generation. Work includes both creating complex coding scenarios and reviewing peer submissions for quality and accuracy., Contributors will:

 * Design and implement multi-file coding tasks across bug fixing, feature development, refactoring, and testing
 * Write clear natural-language specifications and reference implementations
 * Develop and extend unit and integration test suites
 * Review peer-generated tasks for correctness, clarity, and realism
 * Identify edge cases, ambiguities, and potential failure modes
 * Ensure alignment between specifications, code, and expected outputs

Expected Outcomes:

 * High-quality, production-realistic coding tasks
 * Complete and correct reference implementations
 * Robust test coverage and validation artifacts
 * Structured, actionable peer review feedback