Shreya Rajpal
Cofounder And CEO at GuardrailsAI
San Francisco, California, United States
Summary
🤩
Rockstar🎓
Top SchoolShreya Rajpal is a founder and machine learning engineer with 10 years of experience building production AI systems and startups from San Francisco. As Cofounder and CEO of GuardrailsAI and a founding engineer at Predibase, she blends product leadership with deep technical chops in declarative and low-code ML platforms. Her prior roles at Apple and Drive.ai include scaling cross-functional ML data infrastructure and shipping LiDAR perception models in production, demonstrating experience across both research and applied systems. She is an active contributor to Ludwig, where she improved GPU training, device placement, and sequence module portability—work that underscores a focus on performance and practical compatibility for deep learning. Trained at IIT Delhi and UIUC, she pairs rigorous academic foundations with a track record of moving models from prototype to large-scale deployment.
10 years of coding experience
6 years of employment as a software developer
Indian Institute of Technology Delhi (IIT Delhi)Master’s Degree Computer Science, Master’s Degree Computer Science at University of Illinois Urbana-Champaign
English, Hindi
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (7)
neural-network10
pytorch10
machine-learning10
deep-learning10
llm9
python9
llama9
Programming languages (3)
Github contributions (5)
ludwig-ai/ludwig
Aug 2021 - Dec 2022
Low-code framework for building custom LLMs, neural networks, and other AI models
Role in this project:
ML Engineer
ML EngineerContributions:1 release, 307 reviews, 89 commits in 1 year 3 months
Contributions summary:Shreya made significant contributions related to enabling and optimizing GPU training within the Ludwig framework. They modified existing modules for proper GPU conversion and implemented functional GPU training. Their work included fixing failing GPU tests and addressing device placement issues, indicating a focus on performance and compatibility for deep learning model training. Further contributions involved the porting and adaptation of sequence processing modules.
fairness-mlpythonframework-learningdeep-learning-frameworknatural-language-processing
dalianaliu/ludwig
Nov 2021 - Feb 2022
Data-centric declarative deep learning framework
Contributions:27 commits in 3 months
pytorchautoencoderdeep-learningdeclarativemachine-learning
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