Sai Soundararaj
Principal AI Specialist at Tribe AI
Seattle, Washington, United States
Summary
🤩
Rockstar🎓
Top SchoolSai Soundararaj is a Principal AI Specialist and founder with a decade of experience building and advising production-grade ML systems, from leading deep learning teams at Microsoft (Bing & Cortana) to co-founding FloydHub, a YC-backed platform that served 100k+ users and helped standardize DL workflows before cloud providers offered similar services. He combines research pedigree from Stanford AI Lab (under Andrew Ng) with practical engineering—contributing to widely used tooling like the floydhub/dl-docker images that package multi-framework deep learning environments including Jupyter and OpenCV. Based in Seattle, he now splits his time between strategic AI advising, founding Walden (human-in-the-loop well-being AI), and driving AI initiatives at Tribe AI. Known for candid founder reflections on timing and product-market fit, Sai brings both startup grit and enterprise-scale ML leadership to teams tackling real-world AI deployments.
10 years of coding experience
13 years of employment as a software developer
Y CombinatorBS Computer Science, BS Computer Science at Indian Institute of Technology, KharagpurMS Computer Science / EE, MS Computer Science / EE at Stanford University
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (13)
docker10
dockers10
dockerfile10
ubuntu10
tensorflow9
caffe9
opencv8
jupyter8
build-automation7
python7
deep-learning7
kubernetes4
kubernetes-pods4
Programming languages (4)
Github contributions (5)
floydhub/dl-docker
Jun 2016 - Jun 2017
An all-in-one Docker image for deep learning. Contains all the popular DL frameworks (TensorFlow, Theano, Torch, Caffe, etc.)
Role in this project:
DevOps Engineer
DevOps EngineerContributions:39 commits, 9 PRs, 36 pushes in 1 year
Contributions summary:Sai primarily focused on configuring and maintaining the Docker environment for deep learning within the repository. Their contributions include modifying Dockerfiles to define build arguments, setting up configurations for CPU and GPU builds, and merging updates to incorporate new dependencies and tools. The user's work ensures the project's deployment and provides a consistent and reproducible environment for deep learning workflows. They also incorporated configurations for Jupyter notebooks and OpenCV within the Docker images.
mxnetdocker-imagecaffe2deep-learningall-in-one
floydhub/dl-setup
May 2016 - Jan 2017
Instructions for setting up the software on your deep learning machine
Contributions:8 commits, 1 PR, 7 pushes in 8 months
pythonsettingdeep-learninginstructionsmachine-learning
Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.