Tianhang Gao

About Tianhang Gao

I’m currently a software engineer on the Feature Platform team at Snap. I design and build scalable infrastructure and internal feature lifecycle platforms that support the development, deployment, and management of large-scale applications. My work focuses on streamlining engineering workflows, improving system reliability, and enabling teams to build and ship ads/content recommendation products more efficiently.I worked at Twitter Ads Formats team. I have worked cross-functionally to develop 5+ Ad Formats (Carousels, Interactive Text, Deep Linking), top company priorities for building durable Ads business, to help Twitter capture advertiser budgets (millions) and drive better performance and awareness.I graduated from Carnegie Mellon University with Master of Science in Electrical & Computer Engineering. I took Deep Learning, Computer Vision, Web Application Development, Distributed System, and Search Engine classes at CMU. I was a research assistant at CMU CyLab developing deep learning algorithms.I earned my Bachelor\'s degree (EE) at University of Washington Seattle. To support my study as an EE student, I earned a double degree in ACMS: Engr. & Phys. Science to gain strong quantitative reasoning, flexible mathematical analysis, and various computational methods.I am passionate about designing large scale systems and have background in Machine Learning. I developed strong technical skills including Java, Scala Python, C, and MATLAB, as well as Strong analytical, practical, and problem-solving ability during my career.

Experience

1. Software Engineer

   Snap Inc.

   May 2023 — Present · Seattle, WA, US

   Built a quality monitoring service that provides comprehensive tooling for detecting and debugging feature-related issues, with advanced automation and alerting support- Developed a scalable batch enrichment application using Spark, enabling teams to efficiently assemble and experiment with thousands of features without relying on forward fill logic- Led migration of a large-scale feature storage system to optimized partitioning using Apache Iceberg, significantly improving performance and reducing compute/storage costs.

Education

• University of Washington

  Bachelor of Science (B.S.), Electrical Engineering

  2014 — 2018

• Carnegie Mellon University

  Master of Science - MS, Electrical & Computer Engineering

  2018 — 2019

• University of Washington

  Bachelor of Science (B.S.), Applied & Computational Mathematical Sciences: Engr & Phys. Science

  2014 — 2018

Skills

• Software Troubleshooting
• Linux
• Ni Multisim
• Matlab
• Leadership
• Maple
• Verilog
• Microsoft Office
• Python
• Java
• Signal Processing
• Sagemath
• Labview
• Scilab
• Problem Solving
• Data Analysis
• Microsoft Excel
• Powerworld
• Research
• C