LLM Evaluator (Model Response Analyst)

Odixcity Consulting; Portugal; Teletrabalho; EUR 40 000 - 60 000; Tempo integral

Resumo da oferta

Odixcity Consulting seeks an LLM Evaluator to assess and improve large language models. You will judge factuality, coherence, safety, and alignment to guidelines, and provide clear feedback to training teams.

Responsibilities include ranking outputs, comparing prompts, and calibrating scoring with global evaluators to ensure reliability across the team.

Qualificações

• 2+ years in NLP/AI QA, computational linguistics, data analysis or related field.
• Experience crafting prompts to test model limits and behaviors.
• Ability to explain why outputs are good or bad using logical criteria.

Responsabilidades

• Evaluate and rank model-generated text using rubrics on factuality, coherence, safety, and alignment.
• Review multiple outputs per prompt and justify preferred choices.
• Provide actionable feedback to modeling teams on recurring failures.
• Calibrate scoring with fellow evaluators in cross-check sessions.

Conhecimentos

NLP Evaluation

Prompt Engineering

Data Analysis

Quality Assurance

RLHF Data

Inter-rater Calibration

Formação académica

Bachelor's degree in Computer Science or related field

Descrição da oferta de emprego

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 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.