Hello, I’m Grace

AI engineer building the next generation of agent-powered data systems at Hex.

I’m Grace, recent Berkeley EECS grad hyped about leveraging machine learning to transform the way people work and live.


Previously, I interned at Jane Street and PEAK6 as a quantitative trading intern. Prior to my stint in quant finance, I was a software engineering intern at Roblox and a machine learning researcher at Berkeley AI Research Lab advised by Prof. Sergey Levine (Berkeley) and Prof. Kuan Fang (Cornell).


In my spare time, I enjoy playing poker, solving puzzles, and reading. I'm also usually tinkering on some side project. Would love to hear about anything you're working on!


If you'd like to connect, please feel free to add me on LinkedIn here.

Grace Tang headshot
AI Engineer @ Hex • San Francisco

Currently

Resume

Education

Aug 2022 – Dec 2025

University of California, Berkeley

Berkeley, California

  • B.S. Electrical Engineering and Computer Sciences, GPA: 3.9
  • Berkeley Regents’ and Chancellor’s Scholar, Cal Alumni Association Leadership Scholar, Dean’s List
  • Coursework: Algorithms, Data Structures, Operating Systems, Computer Security, Deep Learning, Machine Learning, Machine Structures, Probability and Random Processes, Abstract Linear Algebra, Discrete Mathematics and Probability Theory
  • Qualifier for American Invitational Mathematics Examination (Top 5% of 50,000 AMC test-takers)

Experience

Jun 2025 – Aug 2025

Jane Street Capital

Quantitative Trading Intern · New York City, New York

  • Trained models using Python to predict various equity metadata values from market data.
  • Leveraged data analysis techniques and feature engineering to improve performance of linear regression models and gradient boosted trees.
Jun 2024 – Aug 2024

PEAK6 Capital Management

Quantitative Trading Intern · Chicago, Illinois

  • Learned about options theory through comprehensive education program; investigated and presented options trading strategy.
  • Conducted data analysis with Python to predict future options volatility.
Aug 2023 – Jun 2024

Berkeley Artificial Intelligence Research Lab

Machine Learning Researcher (Python) · U.C. Berkeley, California

  • Worked under Prof. Sergey Levine @ Robotic Artificial Intelligence & Learning Lab (RAIL).
  • Developed robotic system utilizing vision-language models and diffusion models to generalize to a variety of tasks.
May 2023 – Aug 2023

Roblox Corporation

Software Engineering Intern (C#) · San Mateo, California

  • Designed and implemented backend optimizations for Roblox game updates to reduce wait times and increase server stability; decreased wait times for 99.995% of games by over 70%; prevents server crashes for remaining games.
  • Wrote APIs to interact with game update client, enabling full-universe migration with a single button click.

Affiliations & Other

Club Involvement

  • Machine Learning @ Berkeley (Member)
  • Eta Kappa Nu (EECS Honor Society) (Member)
  • Poker@Berkeley (President)
  • Traders@Berkeley (Member)

Projects

Recent work that blends product thinking with execution.

KALIE

Publication · ICRA 2025

  • PyTorch
  • Diffusion Models
  • Vision-Language

Fine-tuning vision-language models for open-world manipulation without robot data.

Built a data generation pipeline leveraging diffusion models to create a diverse 500-image dataset from 50 starting images.

Renjie Poker

  • ReactJS
  • JavaScript
  • HTML
  • CSS

Built a webapp to play a poker-like card game with UI, dealer logic, and statistic tracking.

Leveraged AI-first development to speed up implementation, testing, and deployment.

SET Solver

  • Swift
  • Python
  • UIKit
  • OpenCV
  • PyTorch
  • CoreML

Built iOS machine learning application to find matches in the pattern-recognition card game SET from image.

Utilized OpenCV to build a Python card-classifying program, then applied YOLOv5 object detection to expand use cases.

Personal writings

Miscellaneous thoughts and musings.

See more writing