Alex Sherstinsky
Summary
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RockstarAlex 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
Github Skills (14)
lora10
neural-network10
llm10
pytorch10
machine-learning10
deep-learning10
data-validation10
python10
fine-tuning10
data-engineering10
testing10
natural-language-processing9
data-pipelines9
data-pipeline9
Programming languages (4)
Github contributions (5)
fivetran/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.
ailow-codeneural-networkdeep-learningmachine-learning
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