Nikolay Laptev is an AI research scientist with 11 years of experience building large-scale machine learning and big data systems for companies including Meta, Uber, and Yahoo, and academic stints at Stanford. He designs and ships production-grade models and distributed architectures for time-series forecasting, anomaly detection, recommendation and ranking, improving real-time prediction accuracy by over 70% in prior roles. A frequent invited speaker at top venues (KDD, VLDB, ICDE, Stanford) he blends deep research rigor with hands-on engineering across Hadoop/map-reduce ecosystems and modern agent/LLM work such as Llama. Based in New York, he pairs a strong academic pedigree (UCLA, Stanford postdoc) with a practical track record of moving models from research into high-impact production systems. An often-overlooked strength is his longevity across the stack—from compiler and backend tooling to distributed ML pipelines—demonstrating an ability to simplify hard problems end-to-end.
11 years of coding experience
12 years of employment as a software developer
B.S. M.A. Computer Science Economics, B.S. M.A. Computer Science Economics at UC Santa Barbara
University of California, Los Angeles
Postdoc Electrical Engineering, Postdoc Electrical Engineering at Stanford University
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