Ajay Thampi
Machine Learning Engineer at Meta
California, United States
Summary
🤩
Rockstar🎓
Top SchoolAjay Thampi is a Machine Learning Engineer with 12 years of experience bridging cutting-edge research and production ML systems, currently working on conversational recommendation at Meta Superintelligence Labs. He holds a PhD in Signal Processing and Wireless Communications and has a track record of improving large-scale systems—from ads targeting and fairness at Meta to leading data science at Microsoft and Tractable. His background spans wireless research and industry R&D, including real-world experimental work on USRP platforms and reinforcement learning for LTE, which informs his practical approach to algorithm design. An active open-source contributor, he built and optimized a fast, offline reverse geocoder in Python, parallelizing KD-tree lookups for real performance gains. Based in California, he combines deep academic rigor with product-focused engineering to deliver scalable, responsible AI solutions.
12 years of coding experience
9 years of employment as a software developer
Entrepreneurship, New Venture Creation, Artificial Intelligence, Entrepreneurship, New Venture Creation, Artificial Intelligence at Stanford UniversityBEng, Electrical and Computer Engineering, BEng, Electrical and Computer Engineering at National University of SingaporeDoctor of Philosophy (PhD), Signal Processing and Wireless Communications, Doctor of Philosophy (PhD), Signal Processing and Wireless Communications at University of Bristol
English
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (11)
algorithm10
data-structures10
algorithms10
multiprocessing10
multi-process10
scipy10
python-multiprocessing10
python10
data-structure10
data-processing9
testing8
Programming languages (3)
Github contributions (5)
thampiman/reverse-geocoder
Mar 2015 - Sep 2016
A fast, offline reverse geocoder in Python
Role in this project:
Back-end Developer
Back-end DeveloperContributions:1 release, 60 commits, 14 PRs in 1 year 5 months
Contributions summary:Ajay primarily contributed to the development of a reverse geocoder library. Their work involved implementing core functionalities, including the ability to retrieve administrative region information (admin1 and admin2) based on coordinates. They also added testing code and parallelized the K-D tree implementation for improved performance. Furthermore, the user focused on code cleanup, documentation, and releasing updated versions of the library.
pythongeospatialgeopackagegeocodegeocoder
thampiman/interpretability
Feb 2019 - Jul 2019
Interpretability Techniques to Build Robust AI Applications
Contributions:23 commits, 12 pushes, 2 branches in 4 months
techniquesfairness-mlinterpretabilitydeep-learningreinforcement-learning
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