Review datasets and task outputs against rubrics and guidelines, flag errors and inconsistencies, and document your reasoning where the rules run out. US-based only. 30 openings, remote contractor, $30–60/hour, 3–5+ years in data quality, QA or analysis preferred.
ML systems engineers write and evaluate training tasks for a frontier lab across GPU kernels, performance profiling, distributed debugging and LLM inference serving, plus the rubrics that grade them. 2+ years of hands-on ML infrastructure work. Canada, UK or US; 40 hours a week. $90–120/hour.
Audit applied machine-learning tasks used to train and evaluate a frontier AI lab's models: experiment design, model selection, evaluation methodology, leakage and metric gaming. For US practitioners with 3+ years of hands-on experimental ML in PyTorch, TensorFlow, scikit-learn or XGBoost. $70–90/hour.
New roles land most days. Get them on Telegram or Discord as they are added, or read how the platforms pay before you apply.