6 days ago

AI QA Trainer, LLM Evaluation

Meridial Marketplace, by Invisible

Hybrid
Contractor
$104,000
Hybrid

Job Overview

Job TitleAI QA Trainer, LLM Evaluation
Job TypeContractor
CategoryCommerce
Experience5 Years
DegreeMaster
Offered Salary$104,000
LocationHybrid

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Job Description

AI QA Trainer, LLM Evaluation at Meridial Marketplace, by Invisible

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.

Ideal Candidate Profile

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.

Compensation & Work Details

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

Key skills/competency

  • LLM Evaluation
  • AI Safety
  • Prompt Engineering
  • Adversarial Testing
  • Red-Teaming
  • Python
  • SQL
  • Data Quality Assurance
  • Factual Consistency
  • Regression Testing

Tags:

AI QA Trainer
LLM Evaluation
Model Evaluation
AI Safety
Prompt Engineering
Red-Teaming
Python
SQL
Data Quality Assurance
Regression Testing
Factual Consistency
Bias Auditing
Test Automation
ML/AI Systems
OpenAI Evals
RAG Evaluators
W&B
PyTest
Guardrails
Metrics

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How to Get Hired at Meridial Marketplace, by Invisible

  • Research Invisible's vision: Study their mission, values, recent news, and employee testimonials on LinkedIn and Glassdoor.
  • Tailor your resume: Highlight experience in LLM evaluation, AI safety, and QA frameworks.
  • Showcase practical skills: Provide examples of test plan design, red-teaming, and high-signal bug reporting.
  • Emphasize communication: Demonstrate clear, metacognitive reporting of model failure modes.
  • Prepare for technical deep-dives: Be ready to discuss prompt robustness and data quality assurance.

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