Ryan Zotti
Senior Applied Machine Learning Engineer II at Slickdeals
San Francisco, California, United States
Summary
🤩
Rockstar🎓
Top SchoolRyan Zotti is a senior applied machine learning engineer with 11 years of experience building production ML systems and data infrastructure across fintech, e‑commerce, pharma, and ad tech. He leads end-to-end deep learning recommender work at Slickdeals for millions of users and previously delivered high-impact ML and cloud savings at Neumora by turning 3D brain scans and genomics into actionable models and a patented scan-quality detector. A pragmatic engineer who bridges research and production, Ryan has built real-time search, Kafka/Spark pipelines, and low-latency bidding platforms while shipping GAN prototypes that saved millions in Photoshop automation. He also surfaced seven-figure financial risks and operational savings at LSQ and Capital One by combining rigorous analysis with scalable engineering. Based in San Francisco, he complements his product work with conference presentations, journal co-authorships, and hands-on open-source tinkering (e.g., backend and DevOps on a self-driving car repo).
11 years of coding experience
14 years of employment as a software developer
Bachelor of Science (BSc) Management, Bachelor of Science (BSc) Management at University of Illinois Urbana-Champaign
English
Stackoverflow
1,975reputation
210kreached
7answers
60questions
Github Skills (16)
tornado10
gpio10
api10
apidoc10
python10
ffmpeg9
cli9
command-line9
command-line-interface9
java6
xgboost6
datadog6
machine-learning6
pyspark6
large-language-models6
Programming languages (7)
Github contributions (5)
RyanZotti/Self-Driving-Car
Jul 2016 - Jul 2020
Role in this project:
Back-end Developer & DevOps Engineer
Back-end Developer & DevOps EngineerContributions:124 commits, 226 PRs, 1035 pushes in 4 years
Contributions summary:Ryan primarily contributed to the back-end functionality of the self-driving car project. They implemented a distance sensor API and improved drive control by fixing an infinite loop bug and enabling CLI-adjustable speed. Furthermore, they modified the existing training script, normalizing the inputs for improved accuracy and altering the epoch size. Finally, the user integrated camera functionalities using ffmpeg.
RyanZotti/Self_Driving_RC_Car
Jun 2016 - Jul 2016
Contributions:10 commits, 8 PRs, 48 pushes in 10 days
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