DANIEL NAHAYO DATA ANNOTOTOR EXPERT

Data Annotator

Toloka Annotators

Apr 2026 - Current (4 months)

Data Annotation Expert | LLM Alignment & Quality Assurance Specialist I bridge the gap between frontier AI model capabilities and human intent. As a precision-driven Data Annotation Specialist with a background in Computer Science and Information Systems, I specialize in high-quality data curation, Supervised Fine-Tuning (SFT), and Reinforcement Learning from Human Feedback (RLHF) Throughout my career partnering with industry leaders like Databricks, RWS, and Microsoft, I have focused on improving model reasoning, safety, and alignment. I engineer rigorous Human-in-the-Loop (HITL) workflows, architect multi-turn dialogue rubrics, and document complex edge cases to aggressively mitigate hallucination rates and eliminate dataset bias. My w

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Data Annotator

Luel

Apr 2026 - Current (4 months)

Data Annotator

RWS

Jan 2024 - Current (2 years 7 months)

Engineered high-quality training datasets for frontier Large Language Models (LLMs) through Supervised Fine-Tuning (SFT) and Reinforcement Learning from Human Feedback (RLHF). Authored comprehensive annotation rubrics and established 'Gold Standard' evaluation frameworks to mitigate model hallucinations and eliminate systemic bias. Led end-to-end data quality assurance (QA) audits, mapped complex taxonomies, and validated multi-turn dialogue logic to optimize model alignment and reasoning capabilities for enterprise AI deployment. Curated high-quality training datasets for LLM fine-tuning, specializing in RLHF and complex reasoning tasks to drive model alignment, while integrating human-in-the-loop review within production-ready data pipeli

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Data Management Analyst

Datablics

Jan 2023 - Jan 2024 (1 year 1 month)

Led the development of gold-standard datasets to improve reasoning reliability and mitigate hallucination rates in frontier models, channeling outputs into scalable data pipelines for production evaluation. Engineered SFT (supervised fine-tuning) workflows, converting raw research data into structured, labeled formats (JSON/YAML) for model consumption, and orchestrated Databricks-backed pipelines for versioned dataset management. Optimized labeling efficiency by identifying and closing performance gaps in human-in-the-loop (HITL) feedback cycles, introducing rubric-driven checks that boosted throughput across end-to-end data pipelines.

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Data Annotator

Microsoft Contract

Jan 2019 - Jan 2021 (2 years 1 month)

Validated and labeled massive datasets for AI/ML training, maintaining 100% precision against complex, evolving annotation guidelines, and executed HITL reviews within Databricks-enabled data pipelines to ensure production readiness. Improved downstream model performance by identifying and correcting labeling drift and nuanced edge cases within training sets.

Data Specialist

Sama

Systematized failure-mode labeling, creating detailed documentation for ambiguous reasoning chains to facilitate more accurate model debugging. Bridged the gap between research and production by integrating peer-reviewed insights into daily annotation workflows.

DANIEL NAHAYO

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