Tech Stack

Description

You'll challenge advanced language models on tasks like hallucination detection and factual consistency, documenting failure modes to improve model quality. On a typical day, you will converse with the model on real-world scenarios, verify factual accuracy, design test plans, and suggest improvements to prompt engineering and evaluation metrics.

Requirements

• Bachelor's, master's, or PhD in CS, data science, or related field
• Experience in QA for ML/AI systems, safety/red-team, or test automation
• Hands-on work with LLM eval tooling

Responsibilities

• Evaluate LLMs on hallucination detection and factual consistency
• Design and run test plans and regression suites
• Build clear rubrics and pass/fail criteria
• Capture reproducible error traces with root-cause hypotheses
• Partner on adversarial red-teaming and automation