Dan Iter
Senior Research Scientist at Netflix
Austin, Texas Metropolitan Area United States
Summary
🤩
Rockstar🎓
Top SchoolDan Iter is a Senior Research Scientist with 16 years of experience building large-scale NLP and foundation models, currently advancing foundational model work at Netflix after leading GenAI post-training research at Microsoft. He holds a PhD from Stanford where his work spanned discourse coherence, domain adaptation, and representation learning, and his internships at Google Brain informed practical advances in mixture-of-experts and domain-aware model training. Dan blends deep research rigor with hands-on engineering across startups and enterprise—having shipped systems from kernel-level storage optimizations to distributed AI services—and briefly explored entrepreneurship focused on AI personalization and agentic social networks. Known for obsessing over hyperparameters, he pairs strong empirical instincts with product-facing judgment, able to translate research into robust, production-ready models. Based in Austin, he brings a rare mix of academic depth and operational experience that accelerates teams scaling next-generation language models.
16 years of coding experience
11 years of employment as a software developer
Bronx HS of ScienceB.S. Computer Science, B.S. Computer Science at Columbia EngineeringDoctor of Philosophy - PhD Computer Science, Doctor of Philosophy - PhD Computer Science at Stanford University
Russian, English, Hebrew
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (39)
libfdk-aac10
hydrogen9
nlp8
python8
turkish8
deep-learning8
natural-language-processing8
language-processing8
cython8
text-classification8
named-entity-recognition8
arabic-nlp7
artificial-intelligence7
neural7
machine-learning7
Programming languages (7)
Github contributions (5)
HazyResearch/models
Jun 2016 - Aug 2016
Contributions:72 commits, 57 pushes, 4 branches in 1 month
tensorflow
daniter-cu/daniter-cu.github.io
Dec 2016 - Mar 2025
Contributions:22 pushes, 1 branch in 8 years 4 months
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