Principal ML Scientist Manager - Microsoft Copilot
Bellevue, Washington, United States
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Summary
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Rockstar
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Tanvir Aumi is a Principal ML Scientist Manager at Microsoft leading end-to-end development of Generative AI and agentic experiences for Microsoft Copilot, with a focus on quality, safety, latency, and measurable user impact. He combines hands-on research and people management—setting model strategy, designing evaluation harnesses, and driving fine-tuning/adaptation workflows when prompting falls short. His background in building ultra-low latency, high-scale distributed systems (notably as a backend architect for Microsoft’s Fluid Framework and Azure Fluid Relay) informs production tradeoffs across cost, reliability, and performance. He also established retrieval, vector search, and grounded QA pipelines at Highspot, bringing practical grounding and monitoring patterns to GenAI products. An active contributor to Microsoft’s FluidFramework open-source repo, he has a track record of core backend improvements that enabled real-time collaborative experiences. Based in Bellevue with 11 years of experience and an M.Sc. from the University of Washington, he blends academic rigor with delivery-first engineering.
11 years of coding experience
10 years of employment as a software developer
Master of Science (M.Sc.) Computer Science and Engineering, Master of Science (M.Sc.) Computer Science and Engineering at University of Washington
Bachelor of Science (BSc) Computer Science and Engineering, Bachelor of Science (BSc) Computer Science and Engineering at Bangladesh University of Engineering and Technology
Library for building distributed, real-time collaborative web applications
Role in this project:
Back-end Developer
Contributions:833 reviews, 1615 commits, 196 PRs in 4 years 7 months
Contributions summary:Tanvir's contributions primarily revolve around enhancements and bug fixes within the `microsoft/fluidframework` repository, which focuses on building real-time collaborative web applications. The commits showcase the user's involvement in the routerlicious project, with specific code changes impacting core functionalities. These include modifications to client interactions (e.g., "Stop sending help on client leave"), additions to the snapshot process ("Add op count to snapshot heuristic"), and optimizations for message handling ("Remove traces from stored deltas"), reflecting work on core architecture.
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Tanvir Aumi - Principal ML Scientist Manager - Microsoft Copilot