Jeffrey Tan

Gameplay Engineer @ Riot Games

Malvern, Pennsylvania, United States

About

Hey! I'm Jeffrey, a Computer Science student at Yale University. My current interests are in deep learning and game development.

Experience

  • Gameplay Engineer at Riot Games
    Jan 2026 - Present · 7 mos

  • Gameplay Engineering Intern at Riot Games
    May 2025 - Aug 2025 · 4 mos

    • Developed future sets for Teamfight Tactics, the largest PC strategy game globally with 300 million+ lifetime players. • Upgraded item selection system to accept dynamic data and UI types, making many new gameplay features possible. • Utilized C++ to create new components and workflows for Hextech Engine, Riot’s proprietary game engine. • Facilitated collaboration across design, UX, and art teams to define scope and execute on intern project. • Led intern team at Riot hackathon, winning an award honorable mention from Riot leadership out of over 60 teams.

  • Data Science Intern at Wayfair
    Jun 2024 - Aug 2024 · 3 mos

    • Drove project to analyze and predict metrics for expanding on-page ads, impacting $20 million+ yearly revenue. • Collaborated with stakeholders across many different teams and roles to understand project goals and data pipelines. • Utilized SQL and Python to navigate and draw conclusions from databases containing 1 billion+ user data points. • Won 1st place out of 20 teams in intern innovation challenge by ideating and prototyping furniture assembly assistant.

  • Software Engineer Intern at Quantic
    Jan 2024 - Jun 2024 · 6 mos

    • Developed full-stack application for B2B customer support chat agent rolled out to 50+ small businesses. • Developed complete natural language RAG pipeline, allowing chat agent to interface with domain-specific knowledge. • Created Express and Flask APIs to serve natural language agent, aiming to reduce customer response load by 40%. • Leveraged AWS Computing technologies, MySQL, HTML/CSS/JS, Flask, Express, Milvus.

  • Undergraduate Research Assistant at Gerstein Lab
    Feb 2023 - Jan 2024 · 1 yr

    • Built molecular large language transformer model with multimodal contexts for general molecular analysis tasks. • Constructed novel dataset for molecular text/graph analysis for 160k+ molecules, more than 10x previous benchmarks. • Co-authored paper presenting state of the art NLP model performance. Accepted to the ISMB 2023 conference. • Leveraged AWS Computing technologies, MySQL, Unix/Shell Scripting, and CUDA to expedite research process.