Prajesh Anchalia
Production Engineer at Meta
San Francisco Bay Area United States
Summary
🤩
Rockstar🎓
Top SchoolPrajesh Anchalia is a Production Engineer in the San Francisco Bay Area with experience building and operating large-scale ads and data systems at Yahoo and Meta. He combines strong Big Data and Java expertise with production-first thinking, having led GDPR/CCPA processing and cross-platform ads data pipelines. While pursuing an MS in Computer Science at Columbia remotely, he continues to ship performant backend improvements, including contributions to PyTorch’s performance and compilation logging that demonstrate a knack for low-level debugging and metrics. Prajesh’s background spans production engineering, performance optimization, and project leadership, informed by an early research internship building an OpenStack-based IaaS dashboard. Colleagues rely on him to translate complex compliance and analytics requirements into reliable, observable systems. His profile reflects a pragmatic engineer who pairs academic rigor with hands-on performance tuning in prominent open-source and production environments.
1 year of coding experience
5 years of employment as a software developer
Bachelor of Engineering (B.E.), Computer Science, 9.27/10, Bachelor of Engineering (B.E.), Computer Science, 9.27/10 at R. V. College of Engineering, BangaloreMaster of Science (MS), Computer Science, Master of Science (MS), Computer Science at Columbia University in the City of New York
English, Hindi, Kannada
Github Skills (15)
amazon-dynamodb10
pytorch10
machine-learning10
deep-learning10
dynamodb10
performance-optimization10
python10
induction10
aws-dynamodb10
autograd9
debug9
debugging9
tensor9
gpu8
compiler7
Programming languages (2)
Github contributions (5)
pytorch/pytorch
Sep 2024 - Apr 2025
Tensors and Dynamic neural networks in Python with strong GPU acceleration
Role in this project:
Back-end Developer & Performance Engineer
Back-end Developer & Performance EngineerContributions:13 reviews, 7 PRs, 29 comments in 6 months
Contributions summary:Prajesh primarily contributed to the PyTorch framework's back-end and performance aspects. Their commits focused on improving error handling, such as classifying unsupported dynamic shapes as user errors, and plumbing compile contexts from dynamo.export to aot_compile. They also added logging for compile events and configuration, adding the ability to log and monitor the compilation process, and added logging and collection of metrics related to the inductor FX graph cache. These changes suggest the user works on optimizing performance and debugging compilation processes.
pythongpu-accelerationdeep-learninggpunumpy
ppanchalia/pytorch
Sep 2024 - Apr 2025
Tensors and Dynamic neural networks in Python with strong GPU acceleration
Contributions:45 pushes, 11 branches in 6 months
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