Nikhil Shenoy
Senior Research Engineer at Recursion
Old Toronto, Ontario, Canada
Summary
👤
Senior🎓
Top SchoolNikhil Shenoy is a Senior Research Engineer with eight years of experience building and deploying ML systems at the intersection of generative modelling and molecular science. Currently at Recursion (formerly Research Engineer, now focusing on generative modelling and binding affinity), he combines hands-on research with production-grade engineering—having sped up atomistic simulations and worked on molecular conformation generators at Valence Labs. He has a strong background in applied ML deployments from Wadhwani AI, where he helped ship public-health and agricultural solutions and published work on ML deployment challenges. An active open-source contributor, Nikhil improved maintainability and APIs in the widely used pytorch-lightning codebase, emphasizing robust trainer semantics and documentation. Trained with an MSc (thesis) in Computer Science from UBC and a BTech in Biochemical Engineering from IIT Delhi, he blends domain knowledge in biology with scalable ML engineering to move models from prototype to impact.
8 years of coding experience
3 years of employment as a software developer
Indian Institute of Technology Delhi (IIT Delhi)Delhi Public School, GurgaonMsc. (Thesis), Computer Science, Msc. (Thesis), Computer Science at The University of British Columbia
Github Skills (11)
pytorch10
machine-learning10
deeplearning-ai10
deep-learning10
python10
data-science9
documentation9
refactoring8
api-design8
ai7
testing7
Programming languages (6)
Github contributions (5)
Lightning-AI/pytorch-lightning
May 2022 - Jan 2023
Pretrain, finetune ANY AI model of ANY size on multiple GPUs, TPUs with zero code changes.
Role in this project:
Back-end Developer & DevOps Engineer
Back-end Developer & DevOps EngineerContributions:48 reviews, 12 commits, 15 PRs in 8 months
Contributions summary:Nikhil primarily focused on refactoring and removing deprecated functionalities within the `pytorch-lightning` trainer class, specifically related to checkpointing and other trainer arguments. They updated the codebase, including modifications to the `CallbackConnector` and `trainer.py` files. Additionally, the user removed arguments from the Trainer class and addressed documentation, demonstrating a focus on code maintainability and API improvements. The user's commits also indicate involvement in documentation updates.
pythonheadachespytorch-modelsdata-sciencehandling
shenoynikhil/improve-diffusion-gan
Feb 2023 - Apr 2023
Improving Training Techniques for Diffusion-GAN
Contributions:21 PRs, 112 pushes, 27 branches in 1 month
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