Julie Tibshirani
Staff Software Engineer at Databricks
San Francisco Bay Area United States
Summary
🤩
Rockstar🎓
Top SchoolJulie Tibshirani is a Staff Software Engineer with 11 years of experience building large-scale search and data systems, currently on Databricks' Developer Platform in the San Francisco Bay Area. She has led search platform teams at Sourcegraph and driven core Elasticsearch and Apache Lucene development at Elastic, with deep expertise in vector search, relevance tuning, and high-performance query execution. Her contributions to top open-source projects like Elasticsearch and Lucene include performance refactors, kNN/vector search work, and improving test reliability—skills she applies to production-grade code search, semantic search, and ML evaluation infrastructure. Prior roles at Palantir and research at Stanford underpin her strengths in backend systems, indexing, and platform APIs. A not-obvious detail: she combines hands-on Java/ systems engineering with practical ML-aware search features, bridging traditional IR and emergent LLM-driven code understanding.
11 years of coding experience
13 years of employment as a software developer
Master of Science - MS Computer Science, Master of Science - MS Computer Science at Stanford University
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (22)
algorithm10
lucene10
algorithms10
c-language10
amazon-elasticsearch10
testing10
machine-learning10
indexing10
java10
javas10
elasticsearchquery10
go10
aws-elasticsearch10
elasticsearch10
cprogramming-language10
Programming languages (10)
Github contributions (5)
grf-labs/grf
Aug 2016 - Jun 2021
Generalized Random Forests
Role in this project:
Back-end Developer
Back-end DeveloperContributions:3 releases, 33 reviews, 700 commits in 4 years 11 months
Contributions summary:Julie's commits focused on refactoring and improving the existing codebase of a generalized random forest project. The user removed unused method signatures and includes, updated the public/private visibility of methods, implemented new functionalities, and fixed several bugs. They also implemented and fixed an issue related to a dedicated type of forest used to address a specific research-related application.
random-forestcausal-inferenceeconometricsmachine-learningstatistics
elastic/elasticsearch
Mar 2015 - Nov 2022
Free and Open Source, Distributed, RESTful Search Engine
Role in this project:Back-end Developer
Back-end DeveloperContributions:704 reviews, 990 commits, 968 PRs in 7 years 8 months
Contributions summary:Julie's commits focused on refactoring and improving the performance and stability of the Elasticsearch codebase. They primarily addressed issues related to field collapsing, kNN search, and point-in-time searches, as well as updating the internal code to use the newest Lucene features. The user also worked on improving the test suite by addressing bugs and removing dependencies on deprecated features.
restrestfulsearch-engineelasticsearchjava
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