AI Research & Annotation Specialist (TURING) — RLHF preference ranking and rubric-based evaluation
Conducted advanced data annotation and RLHF preference ranking for analytical and long-form English outputs while maintaining 99% guideline compliance across calibration cycles. Applied rubric-based scoring to assess clarity, coherence, logical reasoning integrity, factual grounding, and instruction adherence. Performed iterative QA audits including hallucination detection, source validation, and chain-of-thought reasoning reviews to improve reliability and reasoning metrics. • RLHF preference ranking for English analytical/long-form outputs • Rubric evaluation for clarity, coherence, reasoning integrity, factuality, and constraint adherence • Hallucination detection and source validation audits • Markdown documentation delivery in Slack to reduce review turnaround