Aditya Paliwal
Member Of Technical Staff at xAI
San Francisco, California, United States
Summary
👤
Senior🎓
Top SchoolAditya Paliwal is a seasoned software engineer and AI/ML infrastructure expert based in San Francisco with 11+ years of experience delivering end-to-end systems at scale. As a Member Of Technical Staff at xAI, he focuses on AI tooling, distributed computing, and production-ready software, bridging research and deployment across ML workflows. His career spans stints at Google as an AI Resident and Citadel Securities as a Quantitative Researcher, blending deep algorithmic insight with scalable software engineering. He is an active contributor to open-source ML tooling, most notably a back-end and DevOps role on the replicate/keepsake project, where he improved YAML-based configuration handling across Python and Go, reduced duplication, and enhanced the CLI experience and tarball extraction for faster checkouts. He holds a Master's in Computer Science from IIIT Bangalore and has demonstrated a track record of turning complex ML pipelines into reliable, auditable production systems. Based in the San Francisco Bay Area, he combines technical depth with hands-on execution across languages and platforms.
11 years of coding experience
3 years of employment as a software developer
International Institute of Information Technology Bangalore
English, Hindi
Stackoverflow
1,819reputation
142kreached
40answers
18questions
permutation
top-5%
Github Skills (15)
version-control10
go10
python10
yaml10
machine-learning9
permutation9
object-detection6
algorithm6
method-chaining6
python-itertools6
tensorflow6
classification6
dockers5
dockerce5
docker5
Programming languages (4)
Github contributions (5)
replicate/keepsake
Nov 2020 - Jan 2021
Version control for machine learning
Role in this project:
Back-end & DevOps Engineer
Back-end & DevOps EngineerContributions:14 reviews, 16 commits, 5 PRs in 1 month
Contributions summary:Aditya primarily focused on enhancing the project's configuration handling, adding support for `.yml` configuration files across both the Python and Go implementations. This involved modifying existing code to accommodate the new file extension, ensuring backward compatibility, and reducing code duplication. The user also worked on improving the Go CLI by implementing features like individual file or directory checkout. Furthermore, they introduced functionalities to extract specific files from tarballs, optimizing the checkout process.
version-controlmachine-learning
CAMP-IIITB/DP-Lecture-1
Jan 2018 - Dec 2018
Introduction to Dynamic Programming
Contributions:18 commits, 2 PRs, 16 pushes in 10 months
cppfmidynamic-programmingwolfram-languagecompetitive-programming
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