Hamid Shojanazeri
Partner Engineering Manager( PyTorch & Llama) at Meta
San Francisco, California, United States
Summary
🤩
Rockstar🎓
Top SchoolHamid Shojanazeri is a Partner Engineering Manager at Meta leading the AI Frameworks Partner Engineering team for PyTorch and Llama, with nine years of experience bridging research-grade models and production systems. He specializes in distributed training (FSDP), model parallelism, and productionizing large transformer models, and has contributed practical FSDP tutorials and TorchServe integrations that make scaling LLMs more accessible to engineers. Based in San Francisco, he combines a PhD in computer vision with hands-on MLOps expertise—implementing sharded checkpoint loading, tensor parallel configs, and custom serving handlers for Hugging Face models. Colleagues rely on him to translate cutting-edge framework features into reliable deployments that meet latency and throughput SLAs.
9 years of coding experience
13 years of employment as a software developer
Master's degree Computer System Engineering, Master's degree Computer System Engineering at UPMDoctor of Philosophy - PhD Computer Vision, Doctor of Philosophy - PhD Computer Vision at Federation University Australia
Stackoverflow
31reputation
16kreached
0answers
1question
Github Skills (27)
continuous-deployment10
torchscript10
pytorch10
distributed-training10
checkpoint10
python10
machine-learning10
sdp10
model-checking10
ml-deployment10
checkpointing10
huggingface-transformers10
mlops10
model-optimization10
classification8
Programming languages (7)
Github contributions (5)
meta-llama/llama-cookbook
Jul 2023 - Mar 2025
Welcome to the Llama Cookbook! This is your go to guide for Building with Llama: Getting started with Inference, Fine-Tuning, RAG. We also show you how to solve end to end problems using Llama model family and using them on various provider services
Role in this project:
Back-end Developer
Back-end DeveloperContributions:247 reviews, 110 PRs, 186 pushes in 1 year 7 months
Contributions summary:Hamid's contributions focused on adding crucial backend configurations and features to the Llama cookbook repository. This includes the implementation of time and tensor parallel (TP) configurations, along with pad token adjustments in the inference scripts. Moreover, the user incorporated FSDP (Fully Sharded Data Parallel) checkpoint loading capabilities, including sharded model loading, indicating a focus on optimizing model deployment and management strategies. The user has modified code related to loading models and preparing them for optimized execution.
aifinetuninglangchainllamallama2
pytorch/serve
May 2020 - Jan 2023
Serve, optimize and scale PyTorch models in production
Role in this project:ML Engineer & MLOps Engineer
ML Engineer & MLOps EngineerContributions:372 reviews, 237 commits, 50 PRs in 2 years 8 months
Contributions summary:Hamid focused on integrating and generalizing Hugging Face transformer models within the PyTorch Serve environment. They developed custom handlers for sequence classification, question answering, and token classification, demonstrating their knowledge of model serialization and deployment. Their work included support for TorchScript, batch inference, and Captum explanations, alongside integrating with the FasterTransformer library. This involved modifying existing code, creating example files, and enhancing documentation, thus streamlining the process of serving large transformer models.
cpupytorchpytorch-modelsservingin-production
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