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Builders & open source

Dmytro Dzhulgakov

Co-Founder, Fireworks AI

Dmytro Dzhulgakov is a co-founder of Fireworks AI, whose team page notes that Dzhulgakov was previously a PyTorch core maintainer at Meta. PyTorch's maintainers page lists Dzhulgakov as an emeritus core maintainer and as an emeritus maintainer of PyTorch's C10 utilities and operator dispatch.12

At a glance

Current role
Co-Founder
Sector
Company
Work listed
1
Sources cited
5
Profile reviewed

Open-source work

Dzhulgakov was a core maintainer of PyTorch while at Meta, and PyTorch's maintainers page now lists Dzhulgakov with emeritus status both among the core maintainers and among the maintainers of C10 utilities and operator dispatch.21

Research

Dzhulgakov co-authored two papers on running deep learning at production scale. A 2018 paper characterized deep learning inference workloads in Facebook's data centers and discussed performance optimizations and hardware implications, and the 2019 paper on the Deep Learning Recommendation Model (DLRM) described a recommendation model with implementations in PyTorch and Caffe2 and a parallelization scheme for its embedding tables.34

Fireworks AI

Dzhulgakov is one of the co-founders of Fireworks AI. The company's team page lists the co-founders with their previous roles, describing Dzhulgakov's as PyTorch core maintainer at Meta.1

Work and contributions

  • PyTorch Open-source code

    Listed on PyTorch's maintainers page as an emeritus core maintainer and an emeritus maintainer of C10 utils and operator dispatch; Fireworks AI's team page describes Dzhulgakov as previously a PyTorch core maintainer at Meta. The repository is under a BSD-style license.215

Timeline

  1. Co-authored a characterization of deep learning inference in Facebook's data centers.3
  2. Co-authored the Deep Learning Recommendation Model (DLRM) paper.4

Other documented roles

Roles the sources document that are not confirmed as current.

  • PyTorch core maintainer, Meta1

Where they work

  • Lin QiaoAlso at Fireworks AI
  • Horace HeML frameworks and libraries · Inference and serving
  • Gavin UbertiInference and serving · ML frameworks and libraries
  • Pete WardenInference and serving · ML frameworks and libraries
  • Joshua LochnerInference and serving · ML frameworks and libraries
  • Mark SaroufimML frameworks and libraries · Inference and serving
  • Georgi GerganovInference and serving · ML frameworks and libraries
  • Ian BuckML frameworks and libraries · Inference and serving
  • Chris LattnerML frameworks and libraries · Inference and serving
  • Jeff JohnsonInference and serving · ML frameworks and libraries

Suggest a correction or removal

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References

5 sources · profile reviewed Oct 21, 2026 · data updated Oct 21, 2026

  1. Fireworks AI — TeamFireworks AI · Official page · accessed Oct 21, 2026
  2. PyTorch Governance | Maintainers (PyTorch 2.14 documentation)PyTorch · Documentation · published Jun 5, 2026 · accessed Oct 21, 2026
  3. Deep Learning Inference in Facebook Data Centers: Characterization, Performance Optimizations and Hardware Implications (arXiv 1811.09886)arXiv · Paper · published Nov 24, 2018 · accessed Oct 21, 2026
  4. Deep Learning Recommendation Model for Personalization and Recommendation Systems (arXiv 1906.00091)arXiv · Paper · published May 31, 2019 · accessed Oct 21, 2026
  5. pytorch/pytorch LICENSE (BSD-style license)PyTorch (GitHub repository) · License · accessed Oct 21, 2026