Software engineer · New York

Yun-Chung (Eric) Liu

I studied Computer Science at Washington University in St. Louis, with a minor in Human-Computer Interaction. After that I spent three years at Morningstar, working on the systems behind their index data. I’m now at Cornell Tech through 2028, and building AI tools and projects on the side.

What I want to do next is still development, just more consumer-facing. I want to be involved in the whole flow myself, seeing where customers actually get stuck, and going in to fix it.

Open to forward-deployed engineering, AI product, and software engineering roles.

  • M.S. Information Systems, Cornell Tech ’28
  • B.S. Computer Science, WashU ’23
  • 3 years at Morningstar
  • Python · Java · React
See what I’m building Get in touch
Illustration of a desk: a monitor showing a dashboard, a laptop, a mug of coffee, a plant, and floating cards for a filing and a cited answer.

Independent work

Projects

Independent September 2026

Footnote

Grounded Q&A over SEC EDGAR filings
  • Python
  • RAG
  • Chroma
  • BM25
  • LLM APIs
  • pytest

You ask a plain-English question about a US public company, and it answers using only that company’s SEC filings. It pulls the filings from EDGAR on demand, chunks them so tables stay intact, and retrieves with a mix of keyword (BM25) and vector search. There’s no RAG framework under it; I wrote that part myself. Every answer links to the filing it came from, and each quote is checked against the source text before it’s shown. If the filings don’t cover the question, it says “not disclosed” instead of guessing. I built an eval set at the same time, and that’s what caught a problem: the thresholds I’d tuned for when to abstain on one company didn’t hold up on the next one.

View the code on GitHub
The Footnote web app: a risk-factors question about QURE answered with a written summary and three verified quotes from its 10-K filings
Footnote answering a risk-factors question, with each quote checked against the cited 10-K before it’s shown. Scroll inside the frame for the full answer.
Independent Sept 2024 – June 2025

FridayFlicks

Spoken movie reviews, scored on-device
  • Android
  • Java
  • TensorFlow Lite
  • Speech-to-text

An Android app that turns a spoken movie review into a rating. It uses Google Assistant Service to transcribe what you say, then runs a TensorFlow Lite sentiment model on the phone to score it. The point was that the rating should come from your actual words, not a star you tap without thinking about it.

View the code on GitHub
FridayFlicks running on Android: a spoken movie review scored by sentiment
Team of 4 Spring 2022

WashU Bear Run

Unity arcade game, concept to deployment
  • Unity
  • C#
  • Game design

A small arcade game built in Unity and C#, start to finish. I led the team of four, ran the design calls, and put together the obstacle dodging, power-ups, and scoring.

Run Bear Run! cover art Run Bear Run! Team of 4 · WashU Bear Run Play on itch.io

Full-time & internships

Experience

Morningstar July 2023 – June 2026
Chicago, IL

Software Engineer

Morningstar Indexes

For three years I worked on the systems behind Morningstar’s index data: the constituents, hedges, and FX rates that clients license and build products on. Most of what I did was connect that data to the people who rely on it. That meant getting files out to clients reliably, building tools so the operations team could see what the data was doing, and working on the client-facing pages. Backend was Java, Spring Boot, and Hibernate; frontend was React.

  • Led the Historical Bulk Delivery system. It got files out to over 1,000 clients and brought data entry down from days to minutes.
  • Built the Portfolio Observability page: index variants, constituents, hedge data, and FX rates in one view, with buttons to reload and recalculate the data. It’s where the team looked first when something seemed off.
  • Built the SmartX data delivery feature in Spring Boot and HCL. It brings in about $50K a year.
  • Built the document table on indexes.morningstar.com/resources, pulling 200+ index documents into one place so clients could find them without asking us.
  • Ran a stretch project pulling data from several teams into one dashboard for engineering leadership, using Morningstar’s internal AI and testing outside vendor datasets to improve outlier detection.
Recreation · layout only, no real data
Portfolio Global Equity Leaders Index
87Constituents
USDBase currency
Net TRIndex variant
Just nowLast recalculated
Rebuilt from memory of the page I shipped at Morningstar — the tabs and buttons work, the data doesn’t.
LB Networks June 2022 – Sept 2022
St. Louis, MO

Software UX Developer Intern

OcularIP

My first job with real customers on the other end. I worked on OcularIP 6.0.4, a network monitoring product used by 130 service providers, adding and fixing features in ReactJS and Material UI. I reworked the graphs on the Subscription Panel to be easier to read and cleaned up its buttons. Small stuff, but the kind of thing people using it every day would notice.

Ford June 2021 – Aug 2021
Shanghai, CN

Data Science Intern

Ford Lincoln, China

The Lincoln team kept asking one question: of everyone who walks into a showroom, who actually buys a car? I ran correlation analysis in SQL across 100,000+ prospective customers, looking at age, showroom visits, test drives, and other factors, and found the three that mattered most for conversion. Then I built a dashboard in ReactJS so managers could dig into it themselves instead of seeing the numbers once in a slide.

Yun-Chung (Eric) Liu

Contact

Get in touch

I’m open to forward-deployed engineering, AI product, and software engineering roles. Happy to talk to anyone working on fintech, data infrastructure, or applied AI.

And if you just want to chat, I’d love to grab a coffee. I’m in New York, but a call works too. Reach out!