The Work
You will guide technical trainers who create and evaluate realistic terminal-based tasks for AI training and evaluation. The work covers coding, systems, simulation, scientific computing, and electrical engineering.
You will own technical quality, mentor the pod, assign work, review revisions, and maintain clear documentation and best practices. You will also monitor quality, throughput, and delivery for a pod of approximately 5 to 10 trainers.
- Review problem statements, codebases, circuit netlists, hardware description languages, datasets, computational environments, reference solutions, and automated tests.
- Validate algorithmic complexity, numerical accuracy, memory management, concurrency, circuit laws, power balance, signal processing, timing, and system stability.
- Check compilers, simulation tools, dependencies, build processes, and graders for reproducibility, meaningful outputs, and resistance to nondeterministic behavior or evaluation bypasses.
- Provide precise technical feedback, track revisions, resolve quality issues, and maintain technical documentation.
What It Pays and Takes
The listing does not provide pay details. This is a contractor, part-time role with a commitment of 20 or more hours per week, and it is open worldwide.
- Workload: 20 or more hours per week.
- Location: Open worldwide.
- Work arrangement: Contractor and part-time.
- Education or experience: A Ph.D., postdoctoral experience, or equivalent advanced technical experience in computer science, electrical engineering, computer engineering, or a related field.
- Programming: Strong ability in C/C++, Rust, Python, Julia, Fortran, MATLAB or Octave, or hardware description languages.
- Systems: Advanced Linux and terminal proficiency.
- Technical background: Hands-on experience in parallel computing, systems programming, scientific software, circuit simulation, signal processing, power systems, digital hardware, or a closely related area.
- Knowledge: Strong understanding of algorithms, computational systems, numerical methods, software testing, or electrical engineering principles.
- Leadership: Experience reviewing complex technical work and mentoring or leading technical teams.
- Helpful tools: MPI, OpenMP, CUDA, LLVM or GCC, CMake, gdb, Valgrind, perf, sanitizers, or Slurm.
- Additional useful experience: Hardware or electrical simulation tools, Docker, Git, CI/CD, pytest, technical benchmarks, automated grading, AI coding agents, open-source maintenance, or LLM-generated technical solutions.
How It Works
To apply, submit your resume on OpenTrain, then complete the application on the hiring site.
About AI Training Work
AI training work is the human side of building AI systems. People create and review examples, code, tests, and model responses so systems can produce more accurate and useful results. OpenTrain helps people find these projects, build a profile, and apply through one place.