Saurav Maheshkar
Member Of Technical Staff at .txt
London, England, United Kingdom
Summary
🤩
Rockstar🎓
Top SchoolSaurav Maheshkar is a Member of Technical Staff based in London with six years of experience building and hardening machine learning systems, developer tools, and research-facing software. He has shipped production-ready services for healthcare startups and led developer advocacy and engineering work at Lightly and Weights & Biases, translating cutting-edge research into reproducible demos and integrations. An active open-source contributor, Saurav has improved reliability and test coverage in major projects like PyTorch Geometric, added core layers to Google’s Trax, and enhanced type safety and CI for Nerfstudio. He blends practical software engineering—secure, scalable web services for medical trials—with deep ML expertise in graph embeddings and NeRFs, having built graph-grounded QA components for medical agents. Known for clear technical writing and community-facing content, he also quietly focuses on tooling and test automation to raise project quality across ecosystems.
6 years of coding experience
2 years of employment as a software developer
Bachelor of Science - BS Computer Science, Bachelor of Science - BS Computer Science at The University of Manchester
English, Hindi
Stackoverflow
182reputation
11kreached
4answers
2questions
Github Skills (36)
unit-testing10
pytorch10
serf10
convolutional-neural-networks10
pytest10
python10
testing10
machine-learning10
numpy10
deeplearning-ai10
deep-learning10
tensorflow10
neural-network10
computer-vision10
jax10
Programming languages (21)
Github contributions (5)
wandb/examples
Nov 2021 - Jan 2023
Example deep learning projects that use wandb's features.
Role in this project:
ML Engineer
ML EngineerContributions:9 reviews, 5 commits, 10 PRs in 1 year 1 month
Contributions summary:Saurav primarily contributes to example projects demonstrating the use of Weights & Biases (wandb) in machine learning workflows. They refactored code, added `wandb.finish()` calls, and integrated wandb functionalities into existing projects involving scikit-learn and TensorFlow. The user also added new Colab notebooks to illustrate the use of wandb with K-Means clustering and weight initialization methods and integrated Tensorboard. Their work focuses on enhancing experiment tracking and visualization using wandb.
pytorchdeep-learningdeep-learning-examplemachine-learningwandb
nerfstudio-project/nerfstudio
Jan 2023 - Apr 2024
A collaboration friendly studio for NeRFs
Role in this project:ML Engineer
ML EngineerContributions:7 reviews, 12 PRs, 30 pushes in 1 year 2 months
Contributions summary:Saurav primarily contributed to improving type hints across the codebase, specifically in the context of NeRF (Neural Radiance Fields) and related components. They updated type hints in engine, cameras, fields, and exporter modules. This involved modifying function signatures, adding type annotations, and addressing type-related issues. Additionally, the user made improvements to the CI/CD pipeline.
pytorchphotogrammetrydeep-learningcomputer-vision3d-reconstruction
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