As a Java Quality Assurance Lead, you will review AI-generated Java code and trainer QA work across Java AI training projects. You will judge work against project guidelines, write clear feedback, monitor contributor quality, and help maintain consistent standards.
You will also support Java review workflows, documentation, onboarding materials, and quality improvements for remote technical teams.
- Review Java code, Spring backend snippets, debugging answers, tests, architecture explanations, and step-by-step reasoning.
- Check compile-time validity, runtime behavior, object-oriented design, API use, debugging accuracy, readability, performance, security, and formatting.
- Review test coverage and assess whether technical explanations are correct and clear.
- Spot-check contributor work, escalate repeated issues, and help keep review guidelines consistent.
- Maintain Java style guides, FAQs, examples, calibration tasks, honeypots, trackers, and onboarding materials.
- Support onboarding, training, and quality communication for remote Java contributors.
What it pays and takes
This is an intermediate-level, part-time contractor role for people with Java development, backend engineering, software QA, code review, or technical mentoring experience.
- Pay: $70 per hour.
- Time: 20 or more hours per week.
- Location: United States only.
- Language: English.
- Strong Java fundamentals, including collections, generics, streams, lambdas, exceptions, concurrency, annotations, and JVM behavior.
- Clear English communication for detailed technical feedback and collaboration.
- Preferred experience with Spring Boot, Maven or Gradle, JUnit, Mockito, Hibernate or JPA, REST APIs, JDBC, GitHub, Docker, CI/CD, logging, and profiling tools.
- A strong plus is experience leading remote technical contributors or working with AI training, data annotation, LLM evaluation, or rubric-based code review.
How it works
Apply on OpenTrain with your resume, then complete the application on the hiring site.
About AI training work
AI training is the human work behind systems that generate and understand code, text, images, and other content. Reviewers assess examples and give precise feedback so models become more accurate, useful, and reliable; people with strong technical expertise are needed for specialist work such as Java code evaluation.
