Nitin Jain
Principal Software Engineering Manager - Teams Productivity Agents at Microsoft
Seattle, Washington, United States
Summary
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Rockstar🎓
Top SchoolNitin Jain is a Senior Engineering Manager based in Seattle with 11 years of experience building cloud-native, AI/ML-driven products and leading high-performing engineering teams at Microsoft and Meta. He has shipped consumer and enterprise features across backend systems, mobile and web apps, and developer productivity tools—most recently driving GenAI (Llama3) initiatives and ML-powered moderation at Meta and launching Money in Excel and Family Safety platforms at Microsoft. Comfortable both as a hands-on contributor and strategic leader, he combines distributed systems and microservices expertise with a strong focus on privacy, developer experience, and customer impact. His open-source contributions to PyTorch quantization—improving LSTM quantization and QAT support—underscore a deep technical fluency in ML model engineering. Known for fostering empathetic, innovation-oriented teams, he repeatedly turns complex research and infrastructure challenges into production-quality products.
11 years of coding experience
11 years of employment as a software developer
Master of Science, Computer Science, Master of Science, Computer Science at University of Cincinnati
Hindi, English
Stackoverflow
197reputation
1.5mreached
6answers
2questions
Github Skills (15)
neural-network10
quantization10
machine-learning10
pytorch10
deep-learning10
python9
autograd8
gpu7
tensor7
android-activity6
android-intent6
merge6
arraylist6
android6
java6
Programming languages (2)
Github contributions (5)
pytorch/pytorch
Mar 2023 - Nov 2024
Tensors and Dynamic neural networks in Python with strong GPU acceleration
Role in this project:
ML Engineer
ML EngineerContributions:1 review, 7 PRs, 4 pushes in 1 year 8 months
Contributions summary:Nitin primarily contributed to the PyTorch repository with a focus on the quantization features, particularly related to LSTM models. Their work included updating the quantizable LSTM implementation to remove unsupported backend operations, fixing issues in the LSTM layer setup for individually observed parts, and enabling QAT (Quantization Aware Training) support. The user also made improvements to make fused modules torchscriptable, ensuring broader compatibility, and addressed related issues with subclassing.
gpu-accelerationneural-networkpythonautogradgpu
Ninja91/pytorch
Mar 2023 - Nov 2024
Tensors and Dynamic neural networks in Python with strong GPU acceleration
Contributions:25 pushes, 6 branches in 1 year 8 months
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