You will create realistic training and evaluation tasks for AI coding models. The work turns real software vulnerabilities into audit-and-patch tasks that test whether models can find, understand, exploit, and securely fix security issues.
- Find and analyze vulnerabilities from CVEs, security advisories, bug reports, and open-source repositories.
- Classify issues with the most specific applicable CWE and relevant OWASP Top 10 categories.
- Reproduce vulnerabilities and write reliable proof-of-vulnerability tests.
- Build Harbor-format audit-and-patch tasks in Python, JavaScript or TypeScript, Java, C or C++, C#, Go, Rust, Ruby, PHP, Kotlin, and Swift.
- Write realistic task instructions and create reproducible Docker environments with pinned dependencies.
- Harden verifiers against gaming and test manipulation.
- Review AI-generated patches and reasoning using technical rubrics.
What it pays and takes
The listing does not provide a pay rate. This is a remote contractor role with part-time employment terms.
- Hours: 20 or more hours per week.
- Location: Open worldwide.
- Language: Professional fluency in English.
- Experience: At least five years of professional software engineering experience.
- Programming: Expert proficiency in at least one supported programming language.
- Security knowledge: Strong knowledge of CWE, CVE, and the OWASP Top 10.
- Testing and analysis: Ability to reproduce vulnerabilities and write proof-of-vulnerability tests; experience with sanitizers, fuzzers, CodeQL, or Semgrep.
- Tools: Strong Git, Linux, Bash, and Docker skills.
- Communication: Strong technical writing and documentation skills.
Helpful background
Experience building security benchmarks, analyzing open-source vulnerabilities, creating automated tests, or evaluating code patches is relevant. The role also calls for independent work in a research-oriented setting.
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 write and understand code. Contributors create examples, tests, and reviews that help models produce safer and more useful results, while experienced specialists bring the technical judgment needed for difficult tasks.