Samyam Rajbhandari
Principal Architect at Snowflake
Redmond, Washington, United States
Summary
🤩
Rockstar🎓
Top SchoolSamyam Rajbhandari is a Principal Architect with 11 years of experience designing and optimizing AI systems, currently based in Redmond and driving architecture at Snowflake after several years at Microsoft. He holds a PhD in Computer Science and has deep hands-on expertise in distributed deep learning, contributing performance-critical CUDA kernels and ZeRO optimizations to the widely used DeepSpeed project. Samyam blends research-grade algorithmic thinking from his doctoral work on tensor contraction and fusion with production-grade MLOps experience—helping deploy low-latency, high-throughput inference via DeepSpeed-MII and Azure integrations. Known for squeezing performance at scale, he has improved checkpointing, memory management, and allreduce performance for large-model training. Colleagues rely on him to translate GPU and parallel-computation theory into reliable, deployable systems that materially reduce training and inference cost.
11 years of coding experience
11 years of employment as a software developer
Cambridge International A-Levels Physics Further Mathematics and Chemistry, Cambridge International A-Levels Physics Further Mathematics and Chemistry at Budhanilkantha SchoolDoctor of Philosophy (PhD) Computer Science and Engineering, Doctor of Philosophy (PhD) Computer Science and Engineering at The Ohio State UniversityBachelor of Arts (B.A.) Physics and Computer Science, Bachelor of Arts (B.A.) Physics and Computer Science at Williams College
English, Nepali, Hindi
Github Skills (26)
algorithm10
pytorch10
deploying10
python10
optimizers10
machine-learning10
inference10
azure-machine-learning10
deepspeed10
mlops10
deep-learning10
optimisation10
cuda10
grpc10
optimization10
Programming languages (3)
Github contributions (5)
deepspeedai/DeepSpeed-MII
Mar 2022 - Nov 2022
MII makes low-latency and high-throughput inference possible, powered by DeepSpeed.
Role in this project:
MLOps Engineer
MLOps EngineerContributions:1 review, 35 commits, 3 PRs in 7 months
Contributions summary:Samyam primarily contributed to the deployment and operationalization of machine learning models within the DeepSpeed-MII framework. Their work involved modifying the `mii/server_client.py` and associated files to integrate gRPC for model serving, enabling asynchronous requests and tensor parallelism. They also implemented features for registering and managing models within an Azure Machine Learning (AML) environment, incorporating model versioning, and supporting different deployment configurations like local and AML-on-AKS. Furthermore, they introduced the ability to enable or disable DeepSpeed optimizations during deployment and implemented features for parallelism configuration.
pytorchdeepspeeddeep-learninginferencelatency
deepspeedai/DeepSpeed
Feb 2020 - Dec 2022
DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.
Role in this project:ML Engineer
ML EngineerContributions:78 reviews, 74 commits, 39 PRs in 2 years 11 months
Contributions summary:Samyam made several contributions focused on optimizing and extending the DeepSpeed library, specifically related to deep learning optimization. They added new CUDA kernels for fused Lamb optimization, indicating involvement in improving training performance. The user also worked on features for ZeRO optimization (stages 2 and 3), including memory management and checkpointing improvements, which are crucial for training large models. Further contributions include debugging and performance enhancements for allreduce operations and gradient accumulation.
billion-parametersfinetuningtrainingmixture-of-expertszero
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