Rafi Ayub
Member Of Technical Staff at Anthropic
Palo Alto, California, United States
Summary
🤩
Rockstar🎓
Top SchoolRafi Ayub is an applied AI engineer with 8 years of experience building production-grade generative models and scalable ML infrastructure, currently at Anthropic after leading GenAI efforts at Meta. He helped found and maintain PyTorch's torchtune for post-training LLM fine-tuning and contributed VQVAE and multimodal features to major open-source PyTorch projects, showing both deep research understanding and practical engineering. His background spans neuroimaging and biomedical ML—publations in predictive medicine and neuropsychiatry—and prior work includes deploying adversarial and generative models for defense applications. Comfortable across research and production, he bridges distributed training systems, model fine-tuning, and multimodal model design, with a habit of shipping tested, end-to-end features that accelerate research-to-production timelines.
8 years of coding experience
8 years of employment as a software developer
Bachelor of Science (BS) Biomedical Engineering, Bachelor of Science (BS) Biomedical Engineering at The University of Texas at DallasMaster's degree Bioengineering, Master's degree Bioengineering at Stanford University
Github Skills (15)
vector10
quantization10
unit-testing10
pytorch10
machine-learning10
pytest10
python10
modeling9
data-processing9
trainings9
multimodal9
configuration-management9
configuration-manager9
nlp8
image-processing8
Programming languages (5)
Github contributions (5)
facebookresearch/multimodal
May 2022 - Aug 2022
TorchMultimodal is a PyTorch library for training state-of-the-art multimodal multi-task models at scale.
Role in this project:
ML Engineer
ML EngineerContributions:227 reviews, 158 commits, 70 PRs in 3 months
Contributions summary:Rafi implemented a vector quantized variational autoencoder (VQVAE) for multimodal data generation within the PyTorch library. Their work included adding a quantization layer, commitment loss, and associated unit tests, demonstrating a strong understanding of VQVAE's core components. The user's contribution involved modifying code related to quantisation and losses, as well as implementing unit tests using Pytest, showcasing an end-to-end process of feature implementation and quality assurance.
pytorchmulti-taskartdeep-learningmultimodal
pytorch/torchtune
Mar 2024 - Mar 2025
PyTorch native post-training library
Role in this project:Back-end Developer
Back-end DeveloperContributions:1 release, 768 reviews, 295 PRs in 1 year
Contributions summary:Rafi updated the requirements in the setup.py file, adding dependencies for sentencepiece and datasets. Additionally, they refactored the config parsing in the finetune_llm.py recipe and related files. The user also added a config validation step in the FullFinetuneParams, ensuring that the arguments are correctly specified. The user also added image handling features to the model.
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