Shikhar Sharma
Principal Applied Scientist at Microsoft
Old Toronto, Ontario, India
Summary
🤩
Rockstar🎓
Top SchoolShikhar Sharma is a Principal Applied Scientist at Microsoft with 13 years of deep learning and ML engineering experience, combining research-grade rigor from an M.Sc. at the University of Toronto with practical product delivery across teams in Montreal and Toronto. He has led and scaled machine learning teams, shipped production-grade AI tooling that boosts productivity and creativity, and supervised numerous interns and engineers. His background spans research scientist roles (including at Maluuba) and long tenure at Microsoft progressing from Research SDE to Principal, reflecting a strong track record of translating novel research into robust systems. An active contributor to NLP tooling and QA—evidenced by improvements to the well-known nlg-eval repository—he brings meticulous testing and cross-version compatibility practices to complex ML codebases. Colleagues rely on him for bridging heavy research with pragmatic engineering and for building infrastructure that keeps models reliable in real-world settings.
13 years of coding experience
8 years of employment as a software developer
Indian Institute of Technology KanpurMaster of Science (M.Sc.) Computer Science, Master of Science (M.Sc.) Computer Science at University of Toronto
Github Skills (10)
testing10
natural-language-generation10
nlp10
pytest10
python10
evaluation10
test-automation10
meteor9
rouge9
machine-translation9
Programming languages (9)
Github contributions (5)
Maluuba/nlg-eval
Jun 2017 - Feb 2020
Evaluation code for various unsupervised automated metrics for Natural Language Generation.
Role in this project:
QA Engineer / Test Automation Engineer
QA Engineer / Test Automation EngineerContributions:15 commits, 13 PRs, 10 pushes in 2 years 7 months
Contributions summary:Shikhar focused on improving the testing infrastructure and ensuring the quality of the `nlg-eval` library. Their contributions include updating and refactoring existing tests to align with new function names and improve clarity. Furthermore, the user added functionality to ensure compatibility with both Python 2 and 3 by modifying codebase to support the same. They also fixed issues related to test data and made changes to replace special characters for the proper working of the METEOR metric.
skip-thought-vectorsmeteorcidercode-evaluationskip-thoughts
kracwarlock/Movie-Recommender-and-Score-Prediction-System
Sep 2013 - Nov 2013
Contributions:28 commits in 1 month
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