Samuel Sherman
ML EngineerStaff AI Engineer
Rapid7
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Staff AI Engineer
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
I am a Machine Learning/AI Engineer with extensive experience in Large Language Models and Generative AI Technology.
University of Colorado Boulder
M. S, Structural Engineering
N/A – Present
University of Vermont
B. S, Civil Engineering
N/A – Present
Rapid7
Staff AI Engineer
March 1, 2026 – Present
Denver, Colorado, United States
Kenzo Security (Acquired by Rapid7)
Founding Engineer
February 1, 2025 – March 1, 2026
VeritasAI
Founder | Owner
September 1, 2024 – Present
Denver, Colorado, United States
Hyphenate
Sr ML Engineer
July 1, 2024 – September 1, 2024
Remote
Project Canary
Senior AI Engineer
August 1, 2023 – June 1, 2024
Denver, Colorado, United States
Workday
Machine Learning Engineer, HCM Skills Machine Learning Team
January 1, 2019 – August 1, 2023
Boulder, Colorado
Amazon
Data Scientist, Hardlines Machine Learning
November 1, 2017 – January 1, 2019
Denver Metropolitan Area
KPMG US
Data Scientist, Tax Ignition Machine Learning Team
July 1, 2016 – November 1, 2017
Denver, CO
Independent Contractor
Data Scientist
February 1, 2016 – June 1, 2016
Denver, CO
Galvanize Inc
Data Science Fellow
October 1, 2015 – January 1, 2016
Denver, CO
High Energy Inc.
Engineer 2
April 1, 2015 – September 1, 2015
Denver, CO
CB&I
Engineer
May 1, 2012 – April 1, 2015
Centennial, CO
University of Colorado
Research Assistant
December 1, 2010 – December 1, 2011
Boulder, CO
University of Vermont
Undergraduate Research Assistant
January 1, 2007 – August 1, 2007
Quest Diagnostics
Assistant (intern)
August 1, 2006 – September 1, 2006
Quest Diagnostics
Biomedical Engineering Assistant (intern)
June 1, 2005 – August 1, 2005
Distracted Driver Detection
June 1, 2016 – July 1, 2016
Using a small, non-diverse labeled dataset of images with different types of distracted driving, built a convolutional neural network to classify the associated state. Applied transfer learning through use of the VGG-16 pre-trained neural network developed by the Visual Geometry Group at The University of Oxford. Achieved the top 7% for log loss score on the State Farm Distracted Driving Kaggle Competition. Tools used include Keras, OpenCV, Cuda, and Theano run on an AWS gpu EC2 instance.
Congressional Bill Modeling
December 1, 2015 – Present
Using The Sunlight Foundation Congress API, GovTrack, and thomas.gov websites, web scraped congressional bill text and retrieved JSON data for votes and bills since congress 103 (1993). Applied Non-Negative Matrix Factorization to derive latent topics from the bill text and modeled the prevalence of different topics since congress 103 to present. Using the important text features and data obtained from JSON files, applied Random Forest and Gradient Boosted ensemble methods to predict the precent of yes votes belonging to a specific party. Applied similar models to predict whether a bill will reach the floor for a vote.
News Article Popularity Predictor
August 1, 2015 – Present
Given the attributes of online news articles from mashable.com, applied a logistic regression and SVM model to predict the probability an article will reach above 50 shares per day.
Master’s Thesis
December 1, 2011 – Present
Developed database relationships using public bridge inventory data from Pontis and NBI. Performed statistical analyses to test accuracy in estimating bridge characteristics from equations applied to data.
Undergraduate Research
May 1, 2007 – Present
Used Paramics, Vissim, and Corsim to analyze traffic data for the city of Burlington and test efficiency of roundabouts. Modeled traffic simulations to develop criteria to warrant the installation of left-turning lanes.
EIT
NCEES
June 24, 2026 – Present
edX Verified Certificate for Scalable Machine Learning
edX
June 24, 2026 – Present
Regression Models
Coursera Course Certificates
June 24, 2026 – Present
Reproducible Research
Coursera Course Certificates
June 24, 2026 – Present
R Programming
Coursera Course Certificates
June 24, 2026 – Present
Exploratory Data Analysis
Coursera Course Certificates
June 24, 2026 – Present
Big Data XSeries
edX
June 24, 2026 – Present
edX Verified Certificate for Introduction to Big Data with Apache Spark
edX
June 24, 2026 – Present
Practical Machine Learning
Coursera Course Certificates
June 24, 2026 – Present
Statistical Inference
Coursera Course Certificates
June 24, 2026 – Present
The Data Scientist’s Toolbox
Coursera Course Certificates
June 24, 2026 – Present
Getting and Cleaning Data
Coursera Course Certificates
June 24, 2026 – Present
Cultural Fit Analysis
The candidate's career trajectory shows a strong inclination towards cutting-edge AI/ML technologies and a willingness to take on challenging roles, including founding a company. Their experience across various companies, from large enterprises (Amazon, Workday) to startups (Kenzo Security, Hyphenate), indicates adaptability to different organizational cultures. The breadth of projects, from academic research to Kaggle competitions and industry applications, suggests a continuous learning mindset and a passion for the field, aligning well with innovative and growth-oriented environments.
Soft Skills & Operational Fit
The candidate's experience as a Founding Engineer and Founder/Owner suggests strong initiative, leadership, and problem-solving skills. Their work on custom metrics and anomaly detection algorithms indicates a detail-oriented and innovative approach. The diverse project portfolio implies adaptability and a proactive learning attitude, which are beneficial for operational fit.