Ethan F.
Cooley

Senior Machine Learning Engineer

linkedin.com/in/ethancooley Get in touch

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00 / About

The
Person

Ethan Cooley

Ethan Cooley is a Senior Machine Learning Engineer based in Charlotte, NC, building AI systems that improve how clinicians make decisions. He studied engineering and computer science at Wake Forest University and is currently pursuing a Master's in AI at Duke University's Pratt School of Engineering.

Outside of work he can be found running ultras in the mountains, competing in triathlons, skiing backcountry, or shredding trails on his mountain bike. He's a brother, a son, and a friend — the people in his life matter more than any finish line or job title.

Charlotte, NC Wake Forest University · Duke MEng AI (in progress) Ultramarathons · Backcountry Skiing · MTB
01 / Experience

Professional
History

Stanson Health

a division of Premier Inc.

Charlotte, NC

Senior Machine Learning Engineer July 2025 — Present
  • Optimize end-to-end LLM inference pipeline through kernel caching and batched sentence embedding strategies, reducing latency and cutting compute costs.
  • Architect and continuously expand a comprehensive functional test suite for LLM models, improving end-to-end coverage and ensuring reliability across model updates and pipeline changes.
  • Lead design, development, and maintenance of the data engine for the data science team, leveraging AWS OpenSearch as a centralized tool for efficient data interaction and retrieval.
  • Architect and implement RESTful API endpoints connecting front-end applications with ML models deployed in AWS SageMaker, enabling rapid access to model predictions and enhancing user experience.
  • Collaborate cross-functionally with clinical product teams and software engineers to define project requirements and deliver solutions aligned with business objectives.
Machine Learning Engineer September 2023 — July 2025
  • Designed and implemented an internal annotation tool enabling healthcare providers to efficiently label cases, directly improving training data quality for production ML models.
  • Established team-wide coding standards by introducing Black and Flake8 across all repositories, fostering a culture of maintainable, consistent code.
  • Built monitoring and logging solutions to track ECR pipeline and SageMaker model performance, leveraging AWS CloudWatch for real-time insights and proactive issue resolution.
  • Authored comprehensive documentation for data engine architecture and API endpoints, and led training sessions to onboard team members to new tools and workflows.
Data Scientist June 2021 — September 2023
  • Built active learning infrastructure that queried uncertain cases from production and surfaced them for annotation, continuously improving model accuracy over time.
  • Employed directed acyclic graphs (DAGs) to streamline and automate annotation jobs, significantly enhancing efficiency of case annotation for ML model training.
  • Utilized NLP techniques to assist clinicians in making medical predictions derived from electronic health record data, directly improving patient care outcomes.
Data Science Intern January 2021 — May 2021
  • Collaborated with senior data scientists to iterate on and improve production ML models, gaining hands-on experience with the full model development lifecycle.
  • Developed and enhanced an internal software app for testing and comparing model predictions, strengthening team understanding of model strengths and weaknesses.

MemoryCrafters LLC

Raleigh, NC

Software Engineering Intern May — August 2020
  • Led full-stack development of a dynamic web app using HTML, CSS, Bootstrap, JavaScript, and Firebase featuring user authentication, scalability, and responsive design.
  • Developed and implemented RESTful APIs to connect with multiple real-time cloud databases.
  • Launched a final product with 275+ active users from 3 countries over 28 days, resulting in new investor interest and acceptance into Launch Chapel Hill accelerator.
02 / Skills

Technical
Stack

Machine Learning

Python PyTorch TensorFlow Hugging Face Scikit-Learn EfficientNet Transfer Learning CNNs NLP Active Learning RAG LLMs

Cloud & Infrastructure

AWS SageMaker AWS ECR AWS S3 AWS OpenSearch AWS CloudWatch Google Cloud Firebase Vercel

Software Engineering

React Node.js JavaScript HTML / CSS Flask FastAPI RESTful APIs OAuth 2.0 Git

Data & MLOps

NumPy Pandas SQL DAGs Model Monitoring A/B Testing Data Pipelines
03 / Education

Academic
Background

Duke University

Master of Engineering — Artificial Intelligence for Product Innovation

Jul 2025 — May 2027  In Progress

Wake Forest University

B.S. in Engineering — Cum Laude

May 2021  ·  Winston-Salem, NC

  • Won best senior capstone project overall — prototyping, presentation, pro-humanitate design, and final delivery. View project →

University of Sydney

Study Abroad — School of Engineering & Computer Science

Fall 2019  ·  Sydney, Australia

04 / Projects

Featured
Work

Trace Personal Project

2025  ·  React, Node.js, Vercel

  • Built a full-stack activity intelligence dashboard connecting to the Strava API — aggregating training history across runs, rides, and swims into a clean, filterable interface with summary statistics.
  • Engineered a secure OAuth 2.0 token exchange flow via a Node.js serverless backend, with automatic token refresh handling so sessions stay alive without re-authentication.
  • Rendered GPS route traces as pure canvas visualizations — no base maps, just the raw polyline — with layered glow effects and an animated card-to-fullscreen transition driven by live screen coordinates.
  • Surfaced sport-specific performance metrics per activity including pace, power, normalized power, heart rate, elevation gain, cadence, and suffer score.
ML Classification of Appendiceal Cancer Team Manager — Senior Capstone

Wake Forest University  ·  Aug 2020 — May 2021

  • Researched and developed ML models to process and classify histopathological images of appendiceal cancer — a rare, frequently misdiagnosed disease.
  • Built an object detection model using Faster-RCNN with Inception-ResNet-v2 to detect cancerous signet ring cells within histopathological images.
  • Developed a deep CNN using TensorFlow/Keras to classify appendiceal cancer subtypes with 85% accuracy vs. avg. pathologist accuracy of 52%.
  • Delivered a Flask/Python web app to Wake Forest Baptist Hospital doctors and pathologists for real-time image upload and model prediction.
Signet Ring Cell Detector slide
View Slides ↗
05 / Beyond Work

Outside
the Office

Ultramarathon Running 48th place — Colorado Run Rabbit Run 50 mile ultra
Triathlon Racing Triathlete — road & time trial
Backcountry Skiing AIARE Level 1 Certified
Mountain Biking Trail & enduro riding
Guitar Self-taught, playing for 10+ years
06 / Contact

Get in
Touch

Interested in working together or just want to say hello? Send a message and I'll get back to you.

↗ linkedin.com/in/ethancooley