Gil Pasternak
Senior Research Scientist at Code Metal
Palo Alto, California, United States
Summary
🤩
Rockstar🎓
Top SchoolGil Pasternak is a Senior Research Scientist based in Palo Alto with seven years of experience applying AI to challenging real-world problems, especially in NLP and interpretability. He blends rigorous theory (MS and applied math background from UC San Diego) with product-facing research, shipping LLM architectures, tooling, and an "AI Tutor" prototype used in undergraduate education. His work spans domains from data quality and cancer genomics to drug discovery, and he has published and led benchmarks on LLM reasoning in biomedical contexts. A practical open-source contributor, he improved documentation and onboarding for the widely used Great Expectations project, showing attention to developer experience as well as models. At Fastino and Code Metal he advanced model architectures and evals, and earlier internships at NVIDIA and Superconductive demonstrate hands-on experience deploying ML systems and pipelines. Colleagues describe him as a creative problem-solver who pairs deep theoretical insight with pragmatic engineering.
7 years of coding experience
4 years of employment as a software developer
University of California, San DiegoDel Norte High School
Github Skills (8)
rs10
data-quality10
restructuredtext10
documentation10
data-engineering10
data-science9
exploratory-data-analysis9
data-profiling8
Programming languages (5)
Github contributions (5)
great-expectations/great_expectations
Sep 2020 - Mar 2021
Always know what to expect from your data.
Role in this project:
Technical Writer
Technical WriterContributions:27 reviews, 9 commits, 21 PRs in 6 months
Contributions summary:Gil's contributions primarily focus on improving the project's documentation. They clarified terminology, fixed broken links, and added explanatory content, including videos and contributing guide enhancements. Their efforts updated documentation related to core concepts, setting up the development environment, and contributing new expectations. The user also updated the documentation to clarify conda installation requirements and self-check error messages.
pythondatadata-integritydatacleanerpipeline-testing
gilpasternak35/Likelihood
May 2020 - Sep 2020
A Data-Quality Monitoring engine that locates data issues and attempts to explain them probabilistically.
Contributions:115 commits, 8 PRs, 108 pushes in 4 months
explaindata-qualitydata-quality-monitoringmonitoringquality
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