Michael A.

Key Skills

Software

Scale AI

Other

Top Subject Matter

Physics & STEM (scientific reasoning, thermodynamics, quantum mechanics)

Biophysics Domain Expertise

protein structure analysis

Top Data Types

Text

Top Task Types

RLHF

Data Collection

Red Teaming

Freelancer Overview

Artificial Intelligence Specialist (Physics & STEM) — Expert annotation, RLHF, and scientific model evaluation. Brings 8+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Scale AI, Other, and Internal. Education includes Bachelor of Science, Texas Christian University (TCU) (2021). AI-training focus includes data types such as Text, Computer Code, and Programming and labeling workflows including RLHF, Data Collection, and Evaluation.

Labeling Experience

Artificial Intelligence Specialist (Physics & STEM) — Expert annotation, RLHF, and scientific model evaluation

Scale AITextRLHF

Provided expert-level labeling, annotation, and evaluation of physics-focused datasets to ensure scientific correctness. Led RLHF workstreams to optimize large language models for advanced scientific reasoning tasks. Mitigated hallucinations in generated mathematical proofs and physical simulations through systematic red-teaming. • Labeled/annotated physics datasets and curated training examples • Evaluated model outputs for adherence to thermodynamics and quantum mechanics • Performed red-teaming for hallucination detection and correction • Supplied high-fidelity feedback loops to refine STEM frontier models

2025 - Present

AI Research Specialist (Computational Physics) — dataset evaluation and scientific model training support

Conducted model validation, bias detection, and multi-modal dataset evaluation to enhance scientific model reliability. Collaborated to deploy AI-driven data analysis platforms that improved processing accuracy for beamline data. Supported physics-informed model training workflows for digital twins and predictive simulation systems. • Validated and evaluated multi-modal scientific datasets • Detected and assessed biases affecting scientific predictions • Improved beamline data processing accuracy via AI platforms • Supported physics-informed model training and reliability checks

2021 - Present

Graduate Research Assistant (Biophysics & AI) - Texas Christian University (TCU)

Scale AITextRLHFRed Teaming

Support biophysics laboratory research by applying machine learning to automate the identification and analysis of protein structures. Prepare and preprocess biological and physical datasets to enable predictive AI modeling for protein structure analysis. Develop and use scripting workflows in Python and R to clean and structure experimental measurements from fluorescence spectrometers for downstream analysis. • Apply machine learning in support of protein structure identification research. • Annotate and pre-process datasets for training predictive models. • Build Python (NumPy, SciPy) and R scripts for spectroscopy data cleaning and structuring. • Contribute to published computational biology and data modeling research outputs.

2019 - 2021

Graduate Research Assistant — Biophysics & AI (data annotation and preprocessing)

OtherData Collection

Annotated and pre-processed biological and physical datasets used to train predictive AI models for protein structure analysis. Supported laboratory research efforts that applied machine learning to automate identification of protein structures. Developed scripts to clean and structure experimental data originating from fluorescence spectrometers. • Annotated/curated biological and physical training datasets • Pre-processed data for predictive protein structure modeling • Built Python and R pipelines for dataset cleaning and structuring • Assisted with ML automation of protein structure identification

2019 - 2021

Education

Work History