Badri Iyengar
Senior Director at Walmart
Saratoga, California, United States
Summary
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Senior🎓
Top SchoolBadri Iyengar is a Senior Director and science-led engineering executive with 17 years of experience building large-scale ML systems for personalization, ads, and decisioning across Uber, Meta, Yahoo and Walmart. He blends deep academic rigor (PhD-level work in compressed sensing and signal processing) with hands-on applied research—driving offline counterfactual policy evaluation, reinforcement learning for billions of daily decisions, and revenue-impacting recommender and ads models. A pragmatic tech leader and mentor, he routinely ships production ML platforms and end-to-end systems (from Spark/Hadoop parameter servers to LLM-driven catalog curation) while coaching cross-functional teams. His open-source contributions include policy-gradient enhancements to Facebook’s ReAgent, reflecting practical expertise in RL and contextual bandits. Known for turning causal inference and optimization research into product velocity gains, he balances technical vision with supportive people management. Based in Saratoga, CA, he thrives at the intersection of algorithmic innovation and measurable business impact.
17 years of coding experience
11 years of employment as a software developer
Doctor of Philosophy (Ph.D.), Doctor of Philosophy (Ph.D.) at University of Wisconsin-Madison
Github Skills (9)
gymnasium10
mle10
openai-gym10
pytorch10
machine-learning10
python10
reinforcement-learning10
ml10
testing9
Programming languages (3)
Github contributions (5)
facebookresearch/ReAgent
Feb 2020 - Nov 2020
A platform for Reasoning systems (Reinforcement Learning, Contextual Bandits, etc.)
Role in this project:
ML Engineer
ML EngineerContributions:9 commits, 1 PR, 1 comment in 8 months
Contributions summary:Badri primarily contributed to the reinforcement learning platform, focusing on implementing and refining the Reinforce algorithm. Their work involved modifications to the `Reinforce` trainer, including default parameter adjustments, off-policy correction, and unit tests. They also made changes to actor components and samplers, specifically for discrete action spaces, and developed a test case for the CartPole environment. The user's contributions demonstrate expertise in policy gradient methods within the context of the ReAgent platform.
reinforcement-learning
badrinarayan/astlinespec
May 2013 - May 2013
Contributions:2 commits in 2 days
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