LLM Evaluator (Model Response Analyst)

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 relating 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 judgement.

· Experience sourcing, cleaning, and annotating datasets specifically for the 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.