Daniel

Key Skills

Software

Data Annotation Tech

Dataturk

Humanatic

iMerit

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Scale AI

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Freelancer Overview

I am a PhD-level applied mathematician and AI specialist with over 10 years of experience supporting AI training, data annotation, data labeling, and model evaluation initiatives across organizations including Scale AI, Invisible Technologies (Outlier), and Turing. My expertise spans the design and annotation of complex mathematical and reasoning datasets, structured data labeling for supervised and reinforcement learning pipelines, validation of AI-generated outputs for logical correctness and pedagogical clarity, and the development of curriculum-aligned training corpora for large language models and tutoring systems. I bring strong machine learning competencies across supervised, unsupervised, and reinforcement learning paradigms; feature engineering and dataset preprocessing; model evaluation using precision, recall, F1-score, ROC-AUC, and calibration analysis; rigorous error analysis and adversarial robustness testing; human-in-the-loop training workflows; prompt engineering and alignment optimization; fine-tuning and instruction tuning of large language models; and data quality assurance through detailed annotation guideline development. Technically, I am highly proficient in Python (NumPy, Pandas, PyTorch, TensorFlow, Scikit-learn), AWS cloud infrastructure, Docker, Kubernetes, CI/CD pipelines, and scalable MLOps architectures. My background in mathematical modeling, formal verification, code review, and structured dataset engineering enables me to build high-integrity data pipelines and scalable production environments that improve model reliability, safety, interpretability, and performance across domains including education, computational reasoning, and autonomous systems. I am passionate about architecting robust data labeling frameworks and end-to-end machine learning systems that power the next generation of AI solutions.

Labeling Experience

Mathematics Specialist (AI Model Training)

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This role focused on designing advanced mathematics problem sets and reviewing AI-generated solutions for accuracy and clarity. The main responsibility involved annotating diverse math datasets to enhance AI learning and providing detailed feedback to improve model reasoning. The experience contributed to the creation of curriculum-aligned datasets for AI-powered tutoring systems. • Designed and annotated problem sets in calculus, algebra, statistics, and discrete mathematics. • Validated AI solutions for correctness, quality, and educational value. • Provided structured and actionable feedback to improve AI reasoning. • Contributed datasets directly used in LLM-based math tutoring environments.

This role focused on designing advanced mathematics problem sets and reviewing AI-generated solutions for accuracy and clarity. The main responsibility involved annotating diverse math datasets to enhance AI learning and providing detailed feedback to improve model reasoning. The experience contributed to the creation of curriculum-aligned datasets for AI-powered tutoring systems. • Designed and annotated problem sets in calculus, algebra, statistics, and discrete mathematics. • Validated AI solutions for correctness, quality, and educational value. • Provided structured and actionable feedback to improve AI reasoning. • Contributed datasets directly used in LLM-based math tutoring environments.

2025

Mathematics Tutor / Senior AI Instructor

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This experience involved training, evaluating, and reviewing AI model outputs in coding and computational reasoning. Duties centered on assessing AI-generated solutions, documenting model errors, and improving model safety and logic. The work directly supported iterative engineering cycles in large language model (LLM) development. • Evaluated and rated AI-generated solutions in Python and Java. • Developed and delivered complex reasoning tasks for LLM assessment. • Documented, categorized, and analyzed model limitations and errors. • Supported audits on safety, consistency, and logical reasoning in LLM outputs.

This experience involved training, evaluating, and reviewing AI model outputs in coding and computational reasoning. Duties centered on assessing AI-generated solutions, documenting model errors, and improving model safety and logic. The work directly supported iterative engineering cycles in large language model (LLM) development. • Evaluated and rated AI-generated solutions in Python and Java. • Developed and delivered complex reasoning tasks for LLM assessment. • Documented, categorized, and analyzed model limitations and errors. • Supported audits on safety, consistency, and logical reasoning in LLM outputs.

2022

Education

Work History