Raj Prateek
Software Engineer, TL at Meta
Menlo Park, California, United States
Summary
🤩
Rockstar🎓
Top SchoolRaj Prateek is a software engineer and technical lead with 11 years of experience building privacy-preserving and machine learning systems at Meta, currently leading Private Matching for Ads. He combines a strong research background in computer vision and ML from Georgia Tech with hands-on production delivery across Ads identity and conversion measurement. At Meta he has bridged FAIR research and large-scale ad infrastructure, and his open-source contributions to facebookresearch/pycls show attention to model performance, code quality, and developer workflows. Raj’s early career includes internships at Palantir, Microsoft, and Amazon, reflecting breadth across systems, telemetry, and payments. He pairs rigorous academic training (BS/MS with top honors) with practical DevOps and backend engineering skills, making him effective at shipping robust, scalable ML products. Notably, he focuses on often-overlooked developer ergonomics—linters, caching, and test instrumentation—to accelerate and stabilize research-to-production paths.
11 years of coding experience
7 years of employment as a software developer
High School Diploma, 12th Grade: 92%; 10th Grade: 9.8/10 CGPA, High School Diploma, 12th Grade: 92%; 10th Grade: 9.8/10 CGPA at DPS Shj, UAEBachelor of Science, Computer Science (AI & Networks), 3.97/4.00, 97th percentile, Highest honors, Bachelor of Science, Computer Science (AI & Networks), 3.97/4.00, 97th percentile, Highest honors at Georgia Institute of Technology
English, Telugu
Github Skills (10)
linting10
codelist10
pytorch10
lint10
batch-normalization10
python10
configuration-management9
machine-learning8
model-optimization8
cicd7
Programming languages (2)
Github contributions (5)
facebookresearch/pycls
Dec 2019 - May 2021
Codebase for Image Classification Research, written in PyTorch.
Role in this project:
Backend Developer & DevOps Engineer
Backend Developer & DevOps EngineerContributions:2 releases, 19 commits, 9 PRs in 1 year 5 months
Contributions summary:Raj's contributions primarily revolve around enhancing the codebase's functionality and maintainability. This includes re-applying and refining changes related to batch normalization parameters, adding a linter using fvcore to enforce code style, and correcting lint issues, indicating a focus on code quality. Further contributions involve integrating features like local caching for model weights and SE blocks for AnyNet, and updating test configurations to include precision time calculations and activation counts, indicating a focus on model performance and development workflows.
pytorchpythondeep-learningcomputer-visionmachine-learning
rajprateek/rajprateek.github.io
Dec 2015 - May 2019
Contributions:41 pushes, 1 branch in 3 years 6 months
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