Nikhila Ravi
Staff Research Engineer at Meta
United States
Summary
🤩
Rockstar🎓
Top SchoolNikhila Ravi is a Staff Research Engineer at Meta with 12 years of experience building foundation models and production-grade computer vision systems. She co-led the influential Segment Anything project and now leads a team focused on visual reasoning, combining research rigor with engineering delivery. Previously she led PyTorch3D efforts at FAIR, contributing fixes for CUDA and texture sampling as well as documentation and build infrastructure for a widely used 3D deep learning library. Her background spans serverless and full‑stack systems, CI/CD and testing (e.g., aws-sdk-mock and AWS Lambda tooling), reflecting a rare blend of low-level performance work and product-focused platform engineering. An experienced conference speaker and reviewer for top vision and ML venues, she pairs first‑class engineering degrees with hands‑on open source stewardship. She also scouts AI startups, signaling an active interest in translating research into impactful products and teams.
12 years of coding experience
7 years of employment as a software developer
University of TokyoVisiting Scholar, Visiting Scholar at Harvard UniversityBachelor's degree, Engineering, First Class Honours with Distinction, Bachelor's degree, Engineering, First Class Honours with Distinction at University of Cambridge
Tamil, English
Stackoverflow
301reputation
31kreached
1answer
1question
Github Skills (35)
pytorch10
javascript10
3d-graphics10
python10
sinon10
operation10
testing10
tensorrt10
aws-lambda10
tensorflow10
mocking10
computer-vision10
nodejs10
tensor10
c-language9
Programming languages (12)
Github contributions (5)
facebookresearch/pytorch3d
Jan 2020 - Jul 2022
PyTorch3D is FAIR's library of reusable components for deep learning with 3D data
Role in this project:
Back-end Developer
Back-end DeveloperContributions:5 releases, 108 commits, 17 PRs in 2 years 6 months
Contributions summary:Nikhila primarily worked on developing the back-end functionality of the project. They made updates to configuration files for documentation using Read the Docs, added a requirements file, and set up the website using Docusaurus, indicating tasks involving build systems, documentation, and front-end site generation. They implemented a fix related to CUDA usage and a fix for texture atlas sampling, which required careful consideration of the data structures and integration with the core functions.
3ddeep-learningpytorch3d
dwyl/learn-aws-lambda
Jan 2016 - Mar 2016
✨ Learn how to use AWS Lambda to easily create infinitely scalable web services
Role in this project:Full-stack Developer
Full-stack DeveloperContributions:25 commits, 9 PRs, 9 pushes in 1 month
Contributions summary:Nikhila focused on adding and testing AWS Lambda functions, demonstrating a clear understanding of serverless architecture. They implemented basic testing frameworks and created mock event objects to simulate various AWS service triggers, including DynamoDB and S3. Furthermore, the user integrated testing into the build process, including CI/CD configuration via Gulp, for automated deployment and verification of changes, ensuring code quality and continuous integration.
aws-lambdalambdawebserviceamazonamazon-web-services
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