ML systems engineer on MTIA software team, developing high-performance LLM inferencing solutions for the MTIA stack

Co-leading the efforts to implement sgl-fusion compilation stack powered by torch compile, dynamo and inductor, optimizing LLMs using kernel fusion and democratizing LLM architecture by developing fusion passes like, Fused Activation (MLP up projection + activation + quantization), Rmsnorm + Quant and more

Lead development of SGLang and Flashinfer on the Rivos Stack

• Evaluated Machine Learning (ML) models on the latest Galaxy devices, with a specific focus

on 3D, NeRF, Gaussian Splatting, and Generative AI applications

• Conducted research and evaluated state-of-the-art papers and projects in the field of 3D imaging and

generative models

• Preprocessed ML training data, ensuring its suitability for 3D and generative tasks

• Actively participated in our innovation process, contributing to prototypes, research papers, and patent

development

• Collaborated closely with other team members to establish an effective pipeline for implementing and

deploying ML solutions

• Proposed creative and innovative solutions to meet product goals, with an emphasis on 3D, NeRF,

Gaussian Splatting and Generative AI

• Created file import nodes for the Geometry Node system. Minimizing disk usage and enabling using Blender as a data visualization tool.

• Refactored existing 3D data importers, exposing functions to retrieve raw mesh data.

• Add CSV import capabilities to Blender and CSV import for geometry nodes.

• Integrated point cloud importers into Geometry nodes.

• maintained CMake build system and added new unit tests to improve coverage