Daniel Fang
Software Engineer Intern at Stealth Startup
Los Angeles, California, United States
Summary
👤
Senior🎓
Top SchoolDaniel Fang is a software engineer focused on large data systems and AI/ML infrastructure, currently interning on Autopilot core infra at Tesla while cofounding a stealth startup aimed at improving long-term memory and human connections across internet profiles. With nine years of experience and an MS in Data Science from Harvard underway, he blends research-oriented machine learning practice (e.g., parsing PubMed and building local vector DBs at Insilico) with hands-on production data work from roles at TerraCycle. He has a track record of building offline, agentic retrieval systems and unified memory architectures rather than one-off chatbots, signaling a preference for durable, privacy-conscious tooling. Based in Los Angeles and grounded in a quantitative background from UCLA in Data Theory and Economics, he moves fluidly between R&D experimentation and scalable engineering. Notably, his startup work explicitly tackles the social design of identity and connection, an uncommon focus for ML-infrastructure builders.
9 years of coding experience
1 year of employment as a software developer
Master of Science - MS Data Science, Master of Science - MS Data Science at Harvard UniversityOregon Episcopal SchoolUniversity of California, Los AngelesProgramming languages (2)
Github contributions (5)
daniel-zm-fang/nutrifind
Sep 2021 - Sep 2021
Contributions:32 commits, 17 pushes, 1 branch in 2 days
daniel-zm-fang/Linear-Algebra-Tools
Apr 2020 - May 2020
Contributions:18 commits, 17 pushes, 1 branch in 15 days
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