Job Title: LLM Evaluator (Model Response Analyst)
Location: Remote (Worldwide)
Job Summary: We are seeking a detail-oriented and analytical LLM Evaluator to assess, analyze, and improve the performance of large language models (LLMs). In this role, you will evaluate AI-generated content for accuracy, coherence, factual reliability, bias, safety, and alignment with defined guidelines.
Responsibilities
* Evaluate and rank model-generated text based on complex rubrics covering dimensions such as factuality, coherence, safety, instruction‑following, and creativity.
* Review multiple model responses to the same prompt and determine which output a human would prefer, providing justifications for your choices.
* Provide clear, concise feedback to the modeling and training teams regarding recurring failure models observed during evaluation sessions.
* Attempt to “break” the model by crafting prompts designed to elicit biased, harmful, or insecure outputs to help patch safety vulnerabilities.
* Collaborate with the quality assurance team to suggest improvements to evaluation guidelines when you encounter ambiguous or unclassifiable edge cases.
* Participate in regular “cross-checking” sessions with other evaluators to calibrate scoring standards and ensure inter‑rater reliability across the global team.
* When a model underperforms, dig deeper than the surface score to hypothesize “why” the model made a specific error (e.g., training data vs. prompt misinterpretation).
* Identify and flag novel or unexpected model behaviors to the research team, contributing to a living library of unique model outputs and failure modes.
Requirements
* Minimum of 2 years of professional experience in a relevant field such as computational linguistics, data analysis, technical writing, quality assurance (specifically for NLP/AI), or cognitive science.
* Bachelor’s degree in Computer Science, or a related field.
* Deep understanding of how to craft prompts to elicit specific behaviors and test model limits.
* Ability to look at a text output and explain “why” it is “good” or “bad” based on logic, tone, factuality, and instruction adherence.
* Experience working with Reinforcement Learning from Human Feedback (RLHF) data collection.
* Proven experience monitoring and improving consistency among evaluation teams. Ability to analyze IAA scores and conduct calibration sessions to align judgment.
* Experience sourcing, cleaning, and annotating datasets specifically for fine‑tuning or evaluating LLMs. Understanding of data distribution and its impact on model performance.
* Familiarity with A/B testing concepts applied to AI. Ability to help design experiments to test if a new model version is truly “better” than the previous one.
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