The Work
You will create practical IntelliJ IDEA tasks and record yourself completing them. The work helps AI systems understand how software developers set up, navigate, debug, test, and maintain projects in an IDE.
Before recording, you will prepare the project files, SDK, dependencies, and reference materials. You will then review the recording and documentation to make sure they are clear, complete, realistic, and aligned with project guidelines.
- Create tasks using Java or Kotlin projects, debugging, project setup, refactoring, testing, Maven or Gradle, Git integration, code navigation, package management, or build troubleshooting.
- Prepare project files, SDKs, dependencies, and references before recording each workflow.
- Record yourself completing the IntelliJ IDEA workflow and explain the technical steps clearly.
- Review recordings and documentation for clarity, completeness, realism, and guideline alignment.
What It Pays And Takes
This is a remote, part-time contractor role for candidates in Bangladesh, India, and Pakistan. Work is completed independently, with reliable delivery across time zones.
- Pay: Up to $25 per hour.
- Time: 20 or more hours per week.
- Location: Open to candidates in Bangladesh, India, and Pakistan.
- Language: Strong written English at B2 level or higher.
- Experience: Entry-level opportunity requiring hands-on IntelliJ IDEA experience with Java or Kotlin.
- Technical skills: Understand project structure, SDKs, run configurations, the debugger, Maven or Gradle, terminal use, code navigation, and version control.
- Required abilities: Manage files, install dependencies, configure an SDK, prepare a reliable recording environment, and explain technical workflows clearly.
- Work habits: Attention to detail, self-direction, and reliable delivery.
- Helpful background: QA, GitHub, DevOps, technical writing, or related computer-based workflows.
How It Works
Apply on OpenTrain with your resume, then complete the application on the hiring site.
About AI Training Work
AI training work is the human side of building artificial intelligence. People prepare examples, record workflows, and review or label information so AI systems can learn how real tasks are done. Technical experience matters because accurate, realistic examples help improve the quality of the resulting models.