ResponsibilitiesOn a typical day you will converse with the model on real-world scenarios and evaluation prompts, verify factual accuracy and logical soundness, design and run test plans and regression suites, build clear rubrics and pass/fail criteria, capture reproducible error traces with root‑cause hypotheses, and suggest improvements to prompt engineering, guardrails, and evaluation metrics (e.g., precision/recall, faithfulness, toxicity, and latency SLOs). QualificationsA bachelor's, master's, or PhD in computer science, data science, computational linguistics, statistics, or a related field is ideal; shipped QA for ML/AI systems, safety/red‑team experience, test automation frameworks (e.g., PyTest), and hands‑on work with LLM eval tooling (e.g., OpenAI Evals, RAG evaluators, W&B) signal fit.