Dylan Sherry
Co-founder And CTO at Stealth Agentic AI Startup
New York, New York, United States
Summary
🤩
Rockstar🎓
Top SchoolDylan Sherry is a seasoned engineering leader and founder with 16 years of experience building ML-driven products, teams, and developer tools from research prototypes to revenue-generating SaaS. As a Co-founder and CTO now at an agentic AI startup in New York, he blends hands-on ML engineering—co-authoring and contributing to the open-source EvalML AutoML project—with organization-level strategy, hiring, and mentorship. He has led large ML engineering orgs at Alteryx to ship enterprise AutoML, driven LLM-powered consumer medical products at ClosedLoop with robust safety and “LLM as judge” evaluation pipelines, and iterated startup ideas into working proofs of concept. Comfortable toggling between C++ system work, Python ML stacks, and product strategy, Dylan is as likely to debug model regressions as to write grant applications that secure funding. His background in distributed genetic programming and creative interactive systems gives him a rare mix of academic rigor and playful product sensibility.
16 years of coding experience
13 years of employment as a software developer
The Recurse CenterMasters of Engineering Computer Science, Masters of Engineering Computer Science at Massachusetts Institute of TechnologyNYSC 2008
Github Skills (13)
scikit-learn10
pandas10
machine-learning10
data-pipeline10
data-pipelines10
automl10
python10
pipe10
pipeline10
scikit10
testing9
hyperparameter-tuning9
documentation8
Programming languages (6)
Github contributions (5)
alteryx/evalml
Jan 2020 - Dec 2022
EvalML is an AutoML library written in python.
Role in this project:
Data Scientist
Data ScientistContributions:7 releases, 817 reviews, 182 commits in 2 years 10 months
Contributions summary:Dylan primarily contributed to the development and improvement of the EvalML library, focusing on machine learning pipeline components and objective functions. Their work involved rewinding and updating pandas versions to diagnose and resolve testing failures. Additionally, the user removed an unused parameter from the objective class, and added the tuner abstract base class, which included related tests and documentation. The user also contributed to the documentation of the project.
pythondata-sciencemodel-selectionoptimizationmachine-learning
dsherry/6867hw2
Oct 2012 - Oct 2012
Contributions:31 commits in 6 days
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