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Staff Machine Learning Engineer at Videa
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Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Experienced machine learning practitioner with 12 years of experience in using data science and machine learning to solve complex problems. Extensive experience in end-to-end development and deployment of machine learning / deep learning models at scale, with deep expertise in the fundamentals of machine learning and software engineering.
University of Notre Dame
Bachelor of Arts, Economics
January 1, 2008 – January 1, 2012
Videa
Staff Machine Learning Engineer
May 1, 2024 – Present
Remote
Videa
Senior Machine Learning Engineer
February 1, 2023 – May 1, 2024
Remote
Granular
Senior Machine Learning Engineer
December 1, 2020 – October 1, 2022
Indianapolis, Indiana, United States
Sallamander 317, LLC
Machine Learning Consultant
October 1, 2019 – December 1, 2020
Indianpolis, IN
Arterys
Machine Learning Lead
October 1, 2018 – May 1, 2019
San Francisco Bay Area
Arterys
Senior Machine Learning Scientist
April 1, 2018 – October 1, 2018
San Francisco Bay Area
Arterys
Machine Learning Scientist
August 1, 2016 – April 1, 2018
San Francisco Bay Area
Galvanize Inc
Data Scientist, Associate Instructor
September 1, 2015 – August 1, 2016
Denver, CO - Platte Campus
Galvanize Inc
Data Science Fellow
July 1, 2015 – September 1, 2015
Denver, CO - Platte Campus
Massachusetts Institute of Technology (MIT)
Research and Consultant Assistant
January 1, 2012 – July 1, 2015
Indianapolis, Indiana
KTorch
July 1, 2019 – Present
A Keras-like interface for training PyTorch networks.
Headline-Generation
June 1, 2016 – Present
- Trained recurrent neural networks to generate headlines from newspaper article text
Motion-Tracking
May 1, 2016 – June 1, 2016
- Trained convolutional neural networks to track motion from one video frame to the next
Building an Early detection Model for Forest-Fires
September 1, 2015 – March 1, 2016
- Used machine learning to determine which "detected fires" from satellite imagery are actually forest-fires - Combined disparate data sets from multiple sources, and created unique features across geographies and times
DataKind Collaboration with World Vision International
March 1, 2015 – March 1, 2016
- Worked with World Vision International to understand and quantify the effects of different economic development programs in third world countries
Cultural Fit Analysis
The candidate's background is heavily skewed towards Machine Learning Engineering and Data Science, with significant experience in deep learning, computer vision, and model deployment. While they have a strong analytical foundation (Economics degree, data science fellowship), the target role of 'Data Analyst' might be a step down in terms of technical depth and responsibility compared to their recent roles. Their project diversity is good within the ML/Data Science domain, but less so for a pure Data Analyst role. The collaboration with World Vision International shows a social impact interest.
Soft Skills & Operational Fit
The candidate's experience as an Associate Instructor and in mentoring ML Scientists suggests strong communication, leadership, and collaboration skills. Their work on FDA-cleared models implies a methodical and quality-focused approach. The project descriptions, while brief, indicate a problem-solving mindset and ability to work on diverse challenges.