Nicolas Koumchatzky
Compute Platform Lead
New York, New York, United States
Summary
👤
Senior🎓
Top SchoolNicolas Koumchatzky is a seasoned AI infrastructure and compute platform leader with over a decade of experience building production-grade machine learning platforms at scale. He led development of Twitter’s deep learning platform (Cortex) and later ran NVIDIA’s MagLev and recommender-systems platform programs, managing globally distributed teams of ~200 engineers. Equally comfortable as a hands-on engineer and executive, he has contributed to core ML tooling—adding autograd capabilities to Torch—and bootsrapped cross-functional initiatives that bridge RAPIDS and deep learning stacks. Based in New York, he hires and mentors talent across distributed systems, data engineering, frameworks, and ML research, and has a rare background combining advanced quantitative training (École Polytechnique, ENSAE) with practical systems delivery. Colleagues describe him as a builder who turns research and prototypes into robust, production-first platforms.
10 years of coding experience
8 years of employment as a software developer
MSc. in Statistics, Financial Mathematics, Statistics, Economics, MSc. in Statistics, Financial Mathematics, Statistics, Economics at ENSAE ParisAdvanced MSc. in Science and Engineering, Mathematics, Finance, Economics, Physics, Advanced MSc. in Science and Engineering, Mathematics, Finance, Economics, Physics at École PolytechniqueClasse Préparatoire, Classe Préparatoire at Lycée Henri IV
English, French
Github Skills (7)
neural-network10
lua10
automatic-differentiation10
pytorch10
autograd10
machine-learning9
testing8
Programming languages (3)
Github contributions (5)
twitter-archive/torch-autograd
Oct 2015 - Jan 2016
Autograd automatically differentiates native Torch code
Role in this project:
ML Engineer
ML EngineerContributions:8 commits, 6 PRs, 20 pushes in 2 months
Contributions summary:Nicolas contributed significantly to the `torch-autograd` repository by adding functionality for automatic differentiation in Torch. Their work includes enabling the `torch.index`, `torch.narrow`, and `torch.select` functions within the autograd system, along with associated testing. They introduced `AutoModule` and `AutoCriterion` classes, allowing for the automated construction and differentiation of custom neural network modules and loss functions defined using standard Lua functions. Further contributions include making these auto-generated modules clonable, savable and loadable.
pytorchautomatic-differentiationtorchmachine-learningautodiff
nkoumchatzky/torch7
Jul 2015 - Feb 2016
Contributions:2 PRs, 8 pushes, 2 branches in 7 months
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