Research Scientist in Generative AI (publishing as Eric Michael Smith)
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Assessing your cultural and operational fit
Google Scholar: https://scholar.google.com/citations?user=uOK8DfQAAAAJ Semantic Scholar: https://www.semanticscholar.org/author/Eric-Michael-Smith/51324296 GitHub: http://github.com/EricMichaelSmith/
Princeton University
Doctor of Philosophy (Ph.D.), Physics
January 1, 2010 – January 1, 2015
Columbia University
Bachelor of Arts (B.A.), Physics
January 1, 2006 – January 1, 2010
Meta
Research Scientist, Generative AI
April 1, 2023 – Present
Meta
Research Engineer at FAIR
March 1, 2018 – April 1, 2023
Blue Apron
Software Engineer in Machine Learning (originally Data Scientist)
September 1, 2015 – February 1, 2018
New York, New York
Insight Data Science
Fellow
June 1, 2015 – August 1, 2015
New York, New York
Princeton University, Department of Physics
Graduate Research Assistant
July 1, 2010 – May 1, 2015
Princeton, New Jersey
Columbia University, Department of Physics
Undergraduate Research Assistant
October 1, 2008 – May 1, 2010
New York, New York
Cornell University, Cornell Center for Materials Research
Undergraduate Research Assistant
June 1, 2008 – August 1, 2008
Ithaca, New York
ericmsmith.net
June 1, 2014 – Present
- Loaded 28 demographic features across 11 datasets into a MySQL database and used forward-selection multiple linear regression along with NumPy, pandas, and matplotlib to show that 3 features alone explain over one-half of the county-level variation (R^2 = 0.54) in vote share for Obama vs. Romney in the 2012 presidential election (code on GitHub) - With NumPy, matplotlib, and pandas for Python, used open data to demonstrate a small but significant correlation (Pearson's r = +0.14) between increased unemployment rate and increased vote share for Obama between the most recent presidential elections
National Day of Civic Hacking
May 1, 2014 – Present
Created prototype code for the NYC Office of Financial Empowerment to join financial counselors' input client data to an existing SQL database
Neural Networks for Machine Learning
Coursera Course Certificates
June 24, 2026 – Present
Cultural Fit Analysis
The candidate's background in academic research and large tech companies like Meta indicates a fit for environments that value innovation, deep technical problem-solving, and structured development. Their personal projects, while demonstrating initiative, are primarily focused on data analysis rather than collaborative software development, which might require adaptation to a more team-oriented ML engineering culture. The target role of ML Engineer aligns well with their professional experience in ML and AI.
Soft Skills & Operational Fit
The candidate's experience as a Research Scientist and Engineer at Meta, along with their role as a Graduate Research Assistant at Princeton, suggests strong analytical, problem-solving, and research skills. Their involvement in spearheading GitHub adoption and explaining complex concepts indicates leadership and communication abilities. The project descriptions imply a methodical approach to data analysis and model development.