You will solve and evaluate Rust backend problems for AI training work. The focus is on whether services are correct, reliable, maintainable, scalable, and ready for production use.
- Solve and validate Rust backend problems involving service behavior, APIs, persistence, concurrency, performance, and reliability.
- Turn functional requirements and performance targets into repeatable implementation and testing workflows.
- Prepare Rust crate workspaces, configuration files, test fixtures, dependency services, databases, and containerized environments.
- Analyze backend behavior and judge correctness, scalability, maintainability, and production readiness.
- Debug complex backend behavior and validate expected failure handling.
What It Pays and Takes
The applicable compensation structure and rate are confirmed for assigned work. The role is suited to someone who can make technical decisions about Rust architecture and backend platforms.
- Pay: The applicable compensation structure and rate are confirmed for assigned work.
- Time: About 15 hours per week, with flexible scheduling. Weekend work is allowed if the work remains active.
- Location: Fully remote and open to candidates in the supported hiring locations listed for this role.
- Language: Fluent in English.
- Experience: Strong practical Rust skills from substantial professional, research, or open-source software work.
- Backend knowledge: Service design, APIs, data systems, concurrency, asynchronous systems, infrastructure, and production software architecture.
- Technical analysis: Ability to assess backend correctness, performance, scalability, or reliability.
- Architecture: Demonstrated ownership of Rust architecture and platform-level technical decisions.
- Helpful experience: Reproducible development and testing environments using databases, dependency services, configuration, and containers.
How It Works
Apply on OpenTrain with your resume, then complete the application on the hiring site.
About AI Training Work
AI training work uses human judgment to improve artificial intelligence systems. In coding projects, experienced developers review software behavior, test results, and model-generated code so systems can become more accurate and reliable.
