ML Engineer Specialist – AI Trainer

Solve applied ML tasks: iterating on problems that test and improve model reasoning, from baseline solutions to optimized workflows. Evaluate scientific writing: compare research abstracts and introductions using review rubrics (e.g., NeurIPS guidelines) for clarity, correctness, and impact. Debug advanced model behavior: probe LLMs on optimization, fairness, regularization, transformers, and applied NLP, documenting failure modes. Design new benchmark tasks: identify datasets, analyze state-of-the-art papers, build evaluation scripts, and provide reference implementations. Improve training datasets: deliver structured feedback that makes AI outputs more accurate, reliable, and useful.