Amy Tai

San Francisco, California, United States

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Summary

🤩

Rockstar

🎓

Top School

Amy Tai is a systems-focused software engineer and researcher with 11 years of experience building scalable infrastructure and databases for large-scale ML and storage systems. She has driven efficiency and inference-engine optimizations at companies from Google and Together AI to Anthropic, and her work spans TPU/resource optimization, fleet simulations, and inference kernels. A PhD-trained systems thinker, Amy has deep hands-on expertise in distributed storage and databases—contributing to projects like CorfuDB and TimescaleDB where she improved transaction semantics, indexing, and Postgres extension behavior. She also enhanced DDlog’s SQL translation and vector-field handling, reflecting comfort with both compiler-level work and low-level storage engineering. Based in San Francisco, she combines research rigor with production-grade delivery—often spotting subtle consistency and pruning issues that others miss.

10 years of coding experience

6 years of employment as a software developer

A.B. Mathematics and Computer Science, A.B. Mathematics and Computer Science at Harvard University

Doctor of Philosophy (PhD) Computer Science, Doctor of Philosophy (PhD) Computer Science at Princeton University

Github Skills (23)

transaction10

datalog10

postgresql10

admin10

databases10

distributed-systems10

java10

javas10

transactionscope10

timescaledb10

sql10

database-design10

rust10

transaction-manager10

database10

Programming languages (11)

TypeScriptJavaDockerfileC++ShellRustCJavaScript

Github contributions (5)

CorfuDB/CorfuDB

Jun 2015 - Nov 2016

A cluster consistency platform

Role in this project:

Back-end Developer

Contributions:56 commits, 10 PRs, 46 pushes in 1 year 5 months

Contributions summary:Amy's contributions primarily focused on enhancing the cluster consistency platform's back-end functionality. They modified the semantics of stream pointers within the SimpleSMREngine, addressing synchronization issues in the LLTxn OCC. The user also rewrote metadata within logging units, implementing and testing features around indexing, including the addition of a B+Tree and support for stream-aware operations. They improved deferred transaction processing and introduced tests related to proper transaction decision-making.

nosqlconsensusstreamingdatastoredistributed-database

timescale/timescaledb

Jun 2018 - Dec 2018

A time-series database for high-performance real-time analytics packaged as a Postgres extension

Role in this project:

Database Engineer / Database Administrator

Contributions:40 commits, 43 PRs, 35 pushes in 5 months

Contributions summary:Amy's contributions center on improving the TimescaleDB database extension. They addressed a bug related to custom scan pruning in lateral joins within the PostgreSQL environment. Additionally, the user made changes to the insert block trigger, modifying it to be a user-visible trigger, improving the functionality of pg_dump and hypertable inserts. They also worked on telemetry functionality and handling alter schema rename commands in the codebase.

postgresql-extensionfinancial-analysiscomplex-queriestime-seriestsdb

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