Alibek Kaliyev

ML Engineer @ Point72 · New York

I'm a machine learning engineer at Point72 in New York. Before that I was at AWS (2024–2026), one of the first engineers on the six-person team that built Amazon Quick Suite's conversational AI from the ground up — and earlier, analytics infrastructure for Amazon Q Business.

My background is in research: three and a half years in the Multifunctional Materials & Machine Learning Group at Lehigh University (PI: Joshua Agar), building physics-constrained neural networks for scanning-probe microscopy — published at a NeurIPS 2023 workshop and in Advanced Materials. I'm now working through a part-time MS in computer science at UT Austin, specializing in machine learning.

I grew up in Kazakhstan and speak English, Russian, and Kazakh. Outside of work, I'm a second-degree black belt.

alibek.t.kaliyev@icloud.com GitHub Google Scholar X Substack LinkedIn CV

Portrait of Alibek Kaliyev

Blog

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I write about what I’m working on and things I’m trying to understand.

Projects

Tools for agents to remember past work, build new capabilities, and connect to everyday data.

News

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  • Nov ’22 Our paper on linear ML models in ferroelectric switching published in Advanced Materials.
  • Oct ’22 Presented our BE-PFM fitting work at the Gulf Coast Undergraduate Research Symposium, Rice University.
  • Oct ’22 Presented Rapid Fitting of BE-PFM at the Fast Machine Learning for Science Workshop, SMU.
  • Sep ’21 Presented at the MSE Undergraduate Research Symposium, Lehigh University.
  • May ’21 Won the Most Novel Research Award at the Drexel AI Research Conference.
  • Apr ’21 Presented at the David and Lorraine Freed Undergraduate Research Symposium, Lehigh University.
  • Oct ’20 Presented at the Gulf Coast Undergraduate Research Symposium, Rice University.
  • Aug ’20 Presented at the Center for Nanophase Materials Sciences User Meeting, Oak Ridge National Laboratory.
  • Jun ’20 Joined the Multifunctional Materials & ML Group at Lehigh as an undergraduate researcher, working on real-time ML for experimental materials science.

Selected Research