Dilshod Tadjibaev
Open Source Engineer Maintainer at Tracel AI / Burn
Cupertino, California, United States
Summary
🤩
Rockstar🎓
Top SchoolDilshod Tadjibaev is an Open Source Engineer and maintainer with 10 years of experience, currently leading development of Burn, a dynamic deep learning framework in Rust focused on compute efficiency and portability. He designs and ships full-stack ML infrastructure—from ops and backends to ONNX import, WASM, no_std and zero-copy weight storage—enabling fast, mixed-precision inference across servers, WASM and embedded devices. His work on burn-onnx and burn-store emphasizes correctness, performance and practical deployment, and he has made no_std tensor support and advanced tensor ops part of the core codebase. Previously he built scalable services and optimized production systems at Amazon and other companies, reducing costs and latency while mentoring teams. Based in Cupertino, he combines systems-level Rust expertise with applied speech recognition experience to move research models into lightweight, production-ready inference.
10 years of coding experience
14 years of employment as a software developer
A.S. Mathematics, A.S. Mathematics at Grossmont CollegeM.S. Computer Science, Computer Science, M.S. Computer Science, Computer Science at San Diego State University
Stackoverflow
1,065reputation
287kreached
10answers
8questions
Github Skills (20)
operation10
tensorrt10
machine-learning10
onnx10
rust-no-std10
tensorflow10
rust10
tensor10
nox10
serialization9
data-serialization9
low-level-programming9
oracle-10g6
oracle6
sql-server6
Programming languages (8)
Github contributions (5)
tracel-ai/burn
Jan 2023 - Mar 2025
Burn is a new comprehensive dynamic Deep Learning Framework built using Rust with extreme flexibility, compute efficiency and portability as its primary goals.
Role in this project:
Back-end Developer & ML Engineer
Back-end Developer & ML EngineerContributions:756 reviews, 290 PRs, 114 pushes in 2 years 1 month
Contributions summary:Dilshod focused on making the `burn-tensor` package no_std compatible, enabling the framework's use in resource-constrained environments. They implemented functionality to create a triangular and diagonal mask. Furthermore, they contributed to supporting different record types in the ONNX import and added the sign tensor operation. The user also added the support for the expand operator for tensors and refined the code generation, ensuring a more robust and versatile foundation for the project.
rustyburnndarraydeep-learningrust
antimora/burn
Feb 2023 - Mar 2025
Burn - A Flexible and Comprehensive Deep Learning Framework in Rust
Contributions:1 PR, 583 pushes, 259 branches in 2 years
Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.