About The Role Support human-in-the-loop AI training workflows for large language models and multimodal systems. You will improve training data quality by completing data labeling, RLHF preference ranking, prompt evaluation, and QA evaluation tasks using structured annotation tools and written guidelines.

Key Responsibilities

• Create and review labeled datasets for NLP, computer vision, and multimodal use cases
• Perform RLHF preference ranking and rubric-based scoring
• Run prompt and response evaluations for helpfulness, factuality, and safety
• Execute content safety labeling (policy, harassment, self-harm, sensitive categories)
• Follow annotation guidelines and document rationales for edge cases
• Conduct QA evaluation via sampling plans, audits, and error taxonomy tracking
• Participate in calibration sessions to reduce rater variance and improve agreement
• Track throughput and quality metrics that impact LLM training pipelines

Required Qualifications

• Strong written English and structured reasoning
• Ability to follow detailed annotation guidelines with consistent judgment
• Comfort using spreadsheets, web tools, or labeling interfaces
• Familiarity with model evaluation and prompt/response patterns
• Attention to detail and evidence-based QA evaluation approach
• Comfort handling sensitive content as part of content safety labeling

Work Details Remote, full-time work delivered through online tooling and written annotation guidelines. Quality is measured via audits, disagreement rates, rubric adherence, and training data quality outcomes.

Compensation

Competitive hourly rate: $30–$50/hr (USD).