Remote AI training and data labeling jobs

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.

Data scientists and analysts analyse datasets, build analytics reports and rebuild data-driven PowerPoint decks so AI learns to turn numbers into clear business insight. US, UK or Canada. 7 openings, contractor, $150–350/hour.

Write point-in-time forecasts on specific swing-state Senate, governor and statewide races, and grade AI political analyses against your own. For state pollsters, campaign analysts and political scientists with live-race experience. US or Canada residents, $150–250/hour.

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.

Evaluate Neuron Kernel Interface (NKI) development tasks for a frontier AI lab: CUDA-to-NKI migration fidelity, Trainium performance optimisation and GPU-versus-Trainium numerical correctness. Requires 2+ years writing NKI kernels for Trainium or Inferentia2. US-only, $70–90/hour.

Evaluate GPU and accelerator kernel development tasks for a frontier AI lab: numerical correctness, benchmarking fairness, task scoping and compile or runtime validity across CUDA, Triton, NKI and Pallas. For US engineers with 3+ years of kernel work in at least two of those frameworks. $70–90/hour.