AI QA Trainer - LLM Evaluation - Freelance Project

$6–$65/hrMeridialRemoteOpen to US residents

At a glance

You evaluate large language models through adversarial and regression testing, verifying factual accuracy, prompt-injection resistance and tool-use correctness, and documenting every failure mode. A degree in computer science, data science, computational linguistics, statistics or a related field is ideal; clear metacognitive communication is essential.

Summary written by AI from the original listing.

  • Remote · Contract role
  • Posted Sep 17, 2025

About the role

Are you an AI QA expert eager to shape the future of AI? Large-scale language models are evolving from clever chatbots into enterprise-grade platforms. With rigorous evaluation data, tomorrow’s AI can democratize world-class education, keep pace with cutting-edge research, and streamline workflows for teams everywhere. That quality begins with you—we need your expertise to harden model reasoning and reliability.

We’re looking for AI QA trainers who live and breathe model evaluation, LLM safety, prompt robustness, data quality assurance, multilingual and domain-specific testing, grounding verification, and compliance/readiness checks. You’ll challenge advanced language models on tasks like hallucination detection, factual consistency, prompt-injection and jailbreak resistance, bias/fairness audits, chain-of-reasoning reliability, tool-use correctness, retrieval-augmentation fidelity, and end-to-end workflow validation—documenting every failure mode so we can raise the bar.

On 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). You’ll also partner on adversarial red-teaming, automation (Python/SQL), and dashboarding to track quality deltas over time.

A 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. Skills that stand out include: evaluation rubric design, adversarial testing/red-teaming, regression testing at scale, bias/fairness auditing, grounding verification, prompt and system-prompt engineering, test automation (Python/SQL), and high-signal bug reporting. Clear, metacognitive communication—“showing your work”—is essential.

Ready to turn your QA expertise into the quality backbone for tomorrow’s AI? Apply today and start teaching the model that will teach the world.

We offer a pay range of $6-to- $65 per hour, with the exact rate determined after evaluating your experience, expertise, and geographic location. Final offer amounts may vary from the pay range listed above. As a contractor you’ll supply a secure computer and high-speed internet; company-sponsored benefits such as health insurance and PTO do not apply.

Employment type: Contract Workplace type: Remote Seniority level: Mid-Senior Level

Applicant countries
Open to US residents

This remote listing states no location restriction, so US residents can apply. Check the original listing for other requirements.

Work arrangement
Remote
Advertised job location
Not specified
Originally posted
2025-09-17
Last checked

How this pay compares

Language & audio: typical hourly pay on each platform, November 2026.

This role $6–$65/hrTypical across platforms $26/hr

  • micro1$48/hr33 rolesMiddle half of roles: $46–$48/hr
  • Mercor$50/hr25 rolesMiddle half of roles: $18–$75/hr
  • Alignerr$23/hr153 rolesMiddle half of roles: $23–$100/hr

Based on 211 hourly-paid roles listed in November 2026. Per-task and project rates are not included. Each platform sets its own pay.

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