Electrical Engineering and Computer Science AI Quality Lead

You will lead quality review for realistic, terminal-based scientific and engineering tasks used to train and evaluate AI systems. The work combines advanced computer science and electrical engineering judgment with hands-on review of technical implementations.

• Lead and mentor a pod of approximately 5 to 10 technical trainers.
• Review problem statements, codebases, circuit netlists, hardware description language code, datasets, reference solutions, computing environments, and automated tests.
• Validate work involving parallel computing, distributed systems, compilers, operating systems, databases, machine learning systems, GPU computing, circuit design, signal processing, power systems, and embedded systems.
• Check algorithm complexity, numerical accuracy, memory management, concurrency, circuit laws, power balance, timing, system stability, and reproducibility.
• Find nondeterminism, race conditions, unreliable thresholds, simulation convergence problems, flawed assumptions, incorrect tolerances, and ways evaluations could be bypassed.
• Review automated graders for functional correctness, performance, frequency response, waveforms, timing, efficiency, and system behavior.
• Give precise feedback, track revisions, onboard trainers, allocate work, monitor throughput, and maintain technical documentation.

What it pays and takes

The role details do not list a pay rate. This is a four-week contractor assignment with a minimum commitment of 20 hours per week and required overlap with Pacific Standard Time.

• Engagement: Contractor assignment lasting four weeks.
• Time: At least 20 hours per week, including four hours of overlap with PST.
• 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 one or more of C, C++, Rust, Python, Julia, Fortran, MATLAB, Octave, or hardware description languages.
• Technical skills: Advanced Linux and terminal use, plus expertise in parallel computing, systems programming, scientific software, circuit simulation, signal processing, power systems, or digital hardware design.
• Knowledge: Algorithms, computational systems, numerical methods, software testing, or electrical engineering principles.
• Review experience: Complex codebases, technical implementations, engineering simulations, or research outputs.
• Leadership: Experience mentoring, reviewing, or leading small technical teams, with strong written communication for precise technical feedback.
• Helpful tools: MPI, OpenMP, CUDA, LLVM, GCC, CMake, gdb, Valgrind, perf, sanitizers, Slurm, electrical simulation tools, Docker, Git, CI/CD, pytest, benchmarks, automated grading, AI coding agents, open-source maintenance, or language model-generated technical solutions.

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 work behind artificial intelligence, including reviewing code, rating model outputs, and testing whether technical systems behave correctly. Specialists help make these evaluations accurate because their subject knowledge lets them spot subtle errors, unsafe assumptions, and unreliable results.