Develop realistic backend coding challenges, including feature implementations and bug fixes, for AI training projects. Design deterministic verifiers, automated tests, and validation criteria that distinguish correct from incorrect solutions. Evaluate code for correctness, reliability, maintainability, security, and performance while identifying edge cases and bottlenecks. Work independently with backend technologies and refine tasks based on project requirements and quality standards.

Debug and improve diverse codebases by fixing software defects, identifying vulnerabilities, conducting security assessments, refactoring legacy code, and optimizing secure backend systems. The specialist will document technical findings and contribute cybersecurity and software engineering expertise to AI training datasets. Work is fully remote and requires independent execution across project specifications.

Research Fellows will validate AI-generated paper reproductions, simulations, and results; map research taxonomies; define standards for high-quality AI-assisted science; and create benchmark tasks and evaluations for frontier AI models. Fellows will contribute to a publicly released benchmark paper and open evaluation set as lead or first authors. The contract offers flexible, remote global work for current STEM PhD students or postdocs with published research and hands-on AI agent experience.