Ayzaan Wahid
Research Engineer at Meta
New York, New York, United States
Summary
🤩
Rockstar🎓
Top SchoolAyzaan Wahid is a research engineer with 11 years of experience building robotic systems and reinforcement learning infrastructure at top AI labs, currently working on Meta Robotics after roles at Google DeepMind and Google Brain. He bridges applied research and production engineering, shipping distributed training improvements and robust model-saving practices for RL toolkits—contributions visible in the widely used tensorflow/agents library. Trained in EECS at UC Berkeley, he specializes in scalable robotics software, distributed TensorFlow workflows, and policy management for real-world control problems. Known for pragmatic solutions that make experimental code reliable in production, he combines deep technical rigor with a focus on repeatability and deployment.
11 years of coding experience
9 years of employment as a software developer
Bachelor of Science (BS) Electrical Engineering and Computer Science, Bachelor of Science (BS) Electrical Engineering and Computer Science at University of California, Berkeley
Github Skills (7)
algorithm10
machine-learning10
distributed-training10
tensorflow10
python10
reinforcement-learning10
git5
Programming languages (7)
Github contributions (5)
tensorflow/agents
Nov 2021 - Aug 2022
TF-Agents: A reliable, scalable and easy to use TensorFlow library for Contextual Bandits and Reinforcement Learning.
Role in this project:
ML Engineer
ML EngineerContributions:5 commits in 9 months
Contributions summary:Ayzaan primarily contributed to the `tf-agents` library, specifically focusing on improving the `Learner` class. Their work involved adding options for `tf.distribute` functionality, allowing for more control over dataset distribution and the use of `RunOptions`. Furthermore, the user addressed potential issues with saving policies by ensuring all policy specification files are saved during the process. The contributions highlight a focus on distributed training and model saving in the context of reinforcement learning.
scalabletf-agentsmultiagent-reinforcement-learningtensorflow-librarymulti-armed-bandits
google-research/ravens
Dec 2020 - Oct 2022
Train robotic agents to learn pick and place with deep learning for vision-based manipulation in PyBullet. Transporter Nets, CoRL 2020.
Contributions:10 reviews, 12 commits, 5 PRs in 1 year 9 months
pythonpybullettensorflowopenai-gymmanipulation
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