Henry C.

Key Skills

Software

Scale AI

Other

Top Subject Matter

Chemistry and mathematics reasoning evaluation (LLM outputs)

Polymer classification and sustainable recycling (ML model building)

Top Data Types

Text

Top Task Types

RLHF

Classification

Entity (NER) Classification

Freelancer Overview

AI / LLM Trainer (Chemistry & Mathematics) - Scale AI (Outlier), Remote. Brings 4+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Scale AI and Other. Education includes Master of Science (Integrated Master’s), Imperial College London and École Normale Supérieure (ENS) (2026) and Secondary School Advanced Level / Pre-University, Westminster School (2021). AI-training focus includes data types such as Text, Computer Code, and Programming and labeling workflows including RLHF, Computer Programming, and Coding.

Labeling Experience

AI / LLM Trainer (Chemistry & Mathematics) - Scale AI (Outlier), Remote

Scale AITextRLHF

Trained large language models by creating and evaluating chemistry and mathematics prompts, rubrics, and reasoning-tree structures to measure output quality. Produced structured human feedback used to score and iteratively improve model responses for accuracy and reasoning quality. Crafted adversarial prompts to induce failure modes and assess how model reasoning changes under challenging inputs. • Prompt and rubric design for chemistry/math tasks • Evaluation and rating of model outputs • Structured human feedback for RLHF-style improvement • Red-teaming via adversarial prompting

2024 - Present

Machine Learning and Chemistry Researcher - ENS Institut des Matériaux Poreux de Paris (IMAP)

Scale AITextClassification

You conducted master’s research focused on machine-learning modeling and chemistry applications. You built and optimized Python models including CNNs, ANNs, random forests, and graph neural networks for classification and regression tasks. You applied these methods to real scientific datasets while developing a biodegradable MOF concept for antibacterial drug delivery. • Developed and optimized ML pipelines in Python for scientific classification and regression • Trained models using architectures such as CNNs, ANNs, random forests, and GNNs • Worked with real scientific datasets to support research objectives • Researched biodegradable metal-organic frameworks for drug delivery oriented to healthcare use

2025 - 2026

Machine Learning Researcher (Polymer Classification) - Imperial College London / Matoha

Other

Developed machine-learning models with an industry partner to classify polymer types for sustainable recycling using scientific data. Implemented and trained classification and regression models in Python to support research and evaluation of separation performance. While not a labeling role explicitly, the work involved preparing datasets and iterating model pipelines for supervised learning tasks. • ML model development for polymer-type classification • Supervised learning pipeline implementation in Python • Dataset preparation and model evaluation for classification • Collaboration with an industry partner on applied ML objectives

2023 - 2024

Education

Work History