Hardik Vala
Founder at One over One
San Francisco, California, United States
Summary
🤩
Rockstar🎓
Top SchoolHardik Vala is a founder and machine learning engineer with 11 years of experience building production ML systems, distributed training, and privacy-preserving federated learning at Apple and Google before founding two startups, one of which was YC S23. He blends research (graduate work in NLP with tier-1 publications) and product execution—driving LLM fine-tuning, retrieval, and MLOps for cloud platforms and Edge-scale recommender systems. Hardik also contributed to tensorflow-federated, improving iterative process composition and dependency alignment for decentralized ML workflows. Known for learning quickly from setbacks (failed early courses, tough interview cycles, and a previous startup flop), he turned those lessons into technical leadership and new ventures in AI agents, memory-augmented models, and distributed model training. Based in San Francisco, he shares practical ML advice for aspiring engineers and holds a BS in Math & Computer Science from McGill.
11 years of coding experience
7 years of employment as a software developer
High School Diploma, High School Diploma at Colonel By Secondary SchoolBachelor of Science - BS, Mathematics and Computer Science, Bachelor of Science - BS, Mathematics and Computer Science at McGill University
Stackoverflow
21reputation
4kreached
2answers
0questions
Github Skills (12)
machine-learning10
tensorflow10
python10
federated-learning10
data-pipelines9
data-pipeline9
algorithm6
dynamic-programming6
docker4
kubernetes4
dockers4
kubernetes-pods4
Programming languages (7)
Github contributions (5)
google-parfait/tensorflow-federated
Jan 2022 - Nov 2022
An open-source framework for machine learning and other computations on decentralized data.
Role in this project:
ML Engineer
ML EngineerContributions:2 releases, 1 review, 24 commits in 9 months
Contributions summary:Hardik contributed to the development of the `tensorflow-federated` repository by modifying and testing code related to iterative process compositions for machine learning on decentralized data. Their work included enhancing the framework to allow datasets within nested computation argument structures in the iterative processing. Furthermore, they addressed dependency conflicts in the notebook tutorials, upgrading versions of `absl-py`, `jax`, and `jaxlib` to align with the project's requirements.
pytorchdeep-learningmachine-learningsecure-computationfederated-learning
hardik-vala/mahjong-solitaire
Jan 2016 - Jun 2021
Contributions:3 pushes, 1 branch, 1 comment in 5 years 5 months
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