Jacob E.

Key Skills

Software

Don't disclose

Scale AI

Top Subject Matter

Large language model (LLM) evaluation and AI training dataset optimization

AI data annotation review and dataset validation

Model evaluation and reliability improvement for ML systems

Top Data Types

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Top Task Types

No task types listed

Freelancer Overview

AI Training Specialist — Alignerr (2025–Present). Brings 14+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Don't disclose and Scale AI. Education includes Master of Information, University of Toronto (2014) and Bachelor of Science in Computer Science, York University (2011). AI-training focus includes data types such as Text and labeling workflows including Evaluation and Rating.

Labeling Experience

AI Training Specialist — Alignerr (2025–Present)

Don't discloseText

Assessed and refined LLM-generated outputs to improve response accuracy and reliability. Strengthened reasoning consistency by reviewing model outputs against expected behavior criteria. Optimized AI training datasets based on evaluation findings to enhance overall model performance. • Evaluated LLM output quality and consistency • Refined training datasets using review insights • Applied structured evaluation to reduce errors • Supported iterative improvements for model reliability

2025 - Present

AI Data Analyst — Outlier AI (2023–Present)

Don't discloseText

Performed response evaluation and annotation review for AI systems to ensure dataset quality. Conducted dataset quality validation to support scalable model training operations. Collaborated on refining labeled data so training outputs met reliability and performance goals. • Reviewed annotations for accuracy and consistency • Validated dataset quality for model training • Evaluated AI system responses against criteria • Supported scalable training workflow readiness

2023 - Present

AI & Software Engineer — Scale AI (2018–2024)

Scale AIText

Contributed to AI workflow optimization, focusing on model evaluation pipeline improvements. Supported efforts to reduce hallucination rates across machine learning applications. Used evaluation feedback to improve model behavior and reliability within AI training workflows. • Enhanced model evaluation pipelines • Reduced hallucination rates via evaluation-driven changes • Optimized AI workflows for reliability • Supported iterative model performance improvements

2018 - 2024

Education

Work History