Abhishek Goswami
Principal Research Engineer at Microsoft AI Frontiers (Research Lab)
Redmond, Washington, United States
Summary
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Rockstar🎓
Top SchoolAbhishek Goswami is a Principal Software Engineer based in Redmond with 11 years of experience building production-grade AI systems and developer tools. He currently leads work on code-generation LLMs, coding agents, and post-training pipelines for Phi-3/3.5/4, and has driven high-performing embedding models that rank top on MTEB benchmarks. At Microsoft he shipped features for GitHub Copilot, implemented LoRA for Codex, and contributed to ML.NET’s core algorithms and validation logic, reflecting deep expertise across ML infrastructure, model tuning, and scalable backend systems. His background spans applied research (indoor localization) to distributed systems and ads prediction, giving him a rare blend of model-level ML know-how and large-scale engineering pragmatism.
11 years of coding experience
10 years of employment as a software developer
Bachelor of Technology (B.Tech.) Computer Science, Bachelor of Technology (B.Tech.) Computer Science at Motilal Nehru National Institute Of TechnologyMaster of Science (M.S.) Machine Learning, Master of Science (M.S.) Machine Learning at Stony Brook University
Github Skills (14)
net10
asp-net10
dotnet10
api10
csharp10
mlnet10
mldotnet10
apidoc10
dotnet-core10
ml9
dotnet-new9
machine-learning9
unit-testing9
algorithms8
Programming languages (6)
Github contributions (5)
dotnet/machinelearning
Jul 2018 - Jun 2019
ML.NET is an open source and cross-platform machine learning framework for .NET.
Role in this project:
Back-end Developer
Back-end DeveloperContributions:63 commits, 105 PRs, 50 pushes in 10 months
Contributions summary:Abhishek's commits center on implementing features and making critical API changes for the `dotnet/machinelearning` repository, which is a .NET-based machine learning framework. They added arguments to a `PipelineSweep Macro`, updating unit tests, and ensuring validations for several column roles. Their work included adding validations for Label, Weight, GroupId and Name columns. Additionally, the user refactored and added new unit tests for multiclass classification.
dotnetmachine-learningml
abgoswam/machinelearning
Aug 2018 - Jun 2019
ML.NET is an open source and cross-platform machine learning framework for .NET.
Contributions:303 pushes, 107 branches in 9 months
dotnetmachine-learning
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