Anuroop Sriram
Founding Member Of Technical Staff at Prometheus
San Francisco, California, United States
Summary
🤩
Rockstar🎓
Top SchoolAnuroop Sriram is an AI research and engineering leader with nine years of experience building production-grade machine learning systems across speech, vision, and chemistry/materials science. As a founding member of technical staff at Project Prometheus and former Research Engineering Lead at Meta FAIR, he has led self-supervised and multilingual speech efforts and steered AI for computational chemistry while also contributing to medical imaging projects like fastMRI. His open-source contributions to fastMRI — including a PyTorch-Lightning refactor, mask fixes, and educational notebooks — show a practical bent for both research engineering and developer enablement. Prior roles at Baidu, Twitter, and academic labs (CMU, IIIT Hyderabad) reflect a strong foundation in ML, NLP, and applied research. Based in the San Francisco Bay Area, he blends deep research expertise with hands-on engineering to move models from prototype to scalable pipelines. Colleagues describe him as someone who pairs rigorous experimentation with pragmatic system design, often surfacing tooling and documentation improvements that accelerate team impact.
10 years of coding experience
13 years of employment as a software developer
Bachelor of Technology (B.Tech.), Computer Science, Bachelor of Technology (B.Tech.), Computer Science at International Institute of Information Technology Hyderabad (IIITH)Master's Degree, Language Technologies (Computer Science), Master's Degree, Language Technologies (Computer Science) at Carnegie Mellon University
English, Hindi, Telugu
Github Skills (10)
net10
pytorch10
machine-learning10
deeplearning-ai10
convolutional-neural-networks10
deep-learning10
mri10
pytorch-lightning9
medical-imaging9
python8
Programming languages (4)
Github contributions (5)
facebookresearch/fastMRI
Nov 2018 - Aug 2020
A large-scale dataset of both raw MRI measurements and clinical MRI images.
Role in this project:
ML Engineer
ML EngineerContributions:7 reviews, 25 commits, 7 PRs in 1 year 8 months
Contributions summary:Anuroop made several contributions related to the core functionality of the fastMRI project. They added an option to apply a mask in the U-Net model, and also refactored the U-Net model to use pytorch-lightning, which streamlines the training process. Furthermore, they added a zero-filled model and a Jupyter notebook tutorial, indicating an interest in model development and user education. They also fixed a bug in the mask implementation and optimized the evaluation pipeline.
clinicalfastmri-datasetfastmrimri-reconstructionneuroimaging
anuroopsriram/anuroopsriram.github.io
Jan 2018 - Jun 2026
Contributions:73 PRs, 83 pushes, 97 branches in 8 years 6 months
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