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 10 years of experience building production ML systems across e-commerce, fintech, and neuroscience, now driving deep-learning recommender work at Slickdeals for 2 million monthly users. He pairs a research mindset—co-author on international journals and a frequent conference presenter—with hands-on engineering, having built end-to-end platforms at The RealReal (Elasticsearch, Kafka, Spark, Docker on GCP) and low-latency ad systems at Capital One. Known for mining insights from unpromising datasets, he has delivered high-impact results like GAN-driven photoshop automation that saved millions and a $1M+ discovery at LSQ that changed business strategy. Equally comfortable in model development and infrastructure, his skill set spans generative AI, deep learning, data engineering, DevOps, AWS/GCP, and he even contributed backend and DevOps work (distance sensor API, ffmpeg camera integration) to a self-driving car project. Based in San Francisco with a BSc in Management from UIUC, he combines product pragmatism with technical depth to consistently move models from prototype to production.
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
Github Skills (9)
tornado10
gpio10
api10
python10
apidoc10
ffmpeg9
cli9
command-line9
command-line-interface9
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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