Software engineer working on accelerating deep learning on specialized AI chips using cutting edge compiler technology (MLIR). Skilled in distributed systems, design, and development of large-scale applications as well as high-performance libraries. Experienced in training huge deep-learning models using Pytorch.

2024 — NowMetaSoftware Engineer - Systems ML (Compiler)

2024 — Now

Menlo Park, California, United States

Working on compiler and framework for Meta's custom AI accelerator chip.

2023 — 2024ModularSoftware Engineer

2023 — 2024

Sunnyvale, California, United States

Modular is the next-generation AI developer platform unifying the development and deployment of AI for the world.

The next generation of product breakthroughs will be powered by production quality infrastructure that brings together the best of compilers and runtimes, is designed for heterogeneous compute, edge to datacenter distribution, and is focused on usability. Unifying software and hardware with a "just works" approach that will save developers enormous time and increase their velocity.

We are incredibly excited about the mission before us, backed by some of the best investors in the world and if you are interested in joining us - just reach out via www.modular.com/careers.

Skills: MLIR · C++ · Python · CMake

2022 — 2022Snap Inc.Research Engineer

2022 — 2022

Santa Monica, California, United States

Developing ML infrastructure for training Graph Neural Networks at scale. Building a framework to train on graph data with more than 500 million nodes.

2021 — 2022Carnegie Mellon UniversityResearch Assistant

2021 — 2022

Pittsburgh, Pennsylvania, United States

Research under Prof. Eric Xing to investigate methods for accelerating large-scale model training using parameter reduction.

Developing solutions to reduce the communication overhead in the distributed training of large-scale models, leveraging matrix factorization and sparsification methods.