Alex Sherstinsky
Co-Founder at ConvoScience
San Francisco Bay Area United States
Summary
🤩
Rockstar🎓
Top SchoolAlex Sherstinsky is a seasoned founder and technical leader with nine years of recent industry experience and a long career bridging ML, data engineering, and product development. Based in the San Francisco Bay Area, he co-founded multiple startups including Qualaroo and GrowthHackers and most recently launched ConvoScience to apply conversation analysis to home services. He was a core contributor and staff engineer at Great Expectations, helping shape the leading open-source data quality library, and has hands-on ML contributions to Ludwig around efficient LLM fine-tuning (LoRA weight merges). His background blends MIT doctoral-level research in complex systems with practical delivery of analytics, ETL, and production ML systems for companies like Directly and Predibase. Known for turning academic rigor into product-ready solutions, he pairs deep technical fluency with operator-focused execution and a knack for making data-driven tooling genuinely useful to nontechnical teams.
9 years of coding experience
31 years of employment as a software developer
B.S. Electrical Engineering and Computer Science, B.S. Electrical Engineering and Computer Science at University of California, BerkeleySc.D. Electrical Engineering and Computer Science, Sc.D. Electrical Engineering and Computer Science at Massachusetts Institute of Technology
Russian
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (14)
neural-network10
lora10
llm10
pytorch10
machine-learning10
deep-learning10
data-validation10
python10
fine-tuning10
data-engineering10
testing10
data-pipelines9
natural-language-processing9
data-pipeline9
Programming languages (4)
Github contributions (5)
great-expectations/great_expectations
Mar 2020 - Jan 2023
Always know what to expect from your data.
Role in this project:
Data Engineer
Data EngineerContributions:9 releases, 3775 reviews, 1352 commits in 2 years 10 months
Contributions summary:Alex appears to be primarily focused on maintaining and improving the Great Expectations library, with a focus on data quality and testing features. Contributions include fixing bugs related to parameter handling, such as rounding and decimal precision, across multiple files. The user's changes highlight a focus on data assistant functionalities and improvements to the metrics and expectations framework, demonstrating a proficiency with the internal components of the Great Expectations project.
pythondatadata-integritydatacleanerpipeline-testing
ludwig-ai/ludwig
Sep 2023 - Sep 2024
Low-code framework for building custom LLMs, neural networks, and other AI models
Role in this project:ML Engineer
ML EngineerContributions:7 releases, 132 reviews, 92 PRs in 1 year
Contributions summary:Alex's commits primarily focus on enhancing the codebase to support merging LoRA weights into base models within the context of a low-code framework for building LLMs and other AI models. They added functionality for merging adapter weights during fine-tuning, which likely improves the efficiency of LLM training. The changes involved modifications to the testing infrastructure, including updates to the test configurations, demonstrating a focus on comprehensive testing of fine-tuning strategies. The user's work enables more advanced fine-tuning capabilities within the Ludwig framework.
fairness-mlpythonframework-learningdeep-learning-frameworknatural-language-processing
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