Mukul Khanna
Machine Learning Engineer at Waymo
Sunnyvale, California, United States
Summary
🤩
Rockstar🎓
Top SchoolMukul Khanna is a machine learning engineer with nine years of hands-on experience at the intersection of embodied AI and computer vision, currently applying his research background at Waymo. He has a strong research pedigree—graduate work with Dhruv Batra on embodied navigation and episodic QA, publications on HDR and surface-normal estimation, and contributions to high-profile open-source projects like Facebook Research's Habitat-Lab where he integrated encoder-decoder CNN features for VQA and navigation baselines. Mukul pairs academic rigor with product-focused internships at Apple and Open Robotics (GSoC), and practical systems experience from robotics teams and industry roles building simulations, control stacks, and vision pipelines. Beyond engineering, he writes technical content, hosted a data-science podcast, and experimented with Zettelkasten-style knowledge sharing, once growing a small but engaged YouTube audience—evidence of his aptitude for communicating complex ideas.
9 years of coding experience
4 years of employment as a software developer
Bachelor of Technology - BTech, Computer Science, 8.3 CGPA (86%), Bachelor of Technology - BTech, Computer Science, 8.3 CGPA (86%) at SRM UniversityMaster's degree, Computer Science, Master's degree, Computer Science at Georgia Institute of Technology
Stackoverflow
13reputation
4kreached
2answers
3questions
Github Skills (20)
pytorch10
python10
mask-rcnn10
deep-learning10
faster-rcnn10
computer-vision10
reinforcement-learning9
ai9
deep-reinforcement-learning9
simulator7
robotics7
object-detection6
heroku6
hdr6
opencv6
Programming languages (9)
Github contributions (5)
facebookresearch/habitat-lab
Sep 2020 - Jan 2023
A modular high-level library to train embodied AI agents across a variety of tasks and environments.
Role in this project:
ML Engineer
ML EngineerContributions:36 reviews, 14 commits, 20 PRs in 2 years 4 months
Contributions summary:Mukul implemented and integrated a pre-trained Encoder-Decoder CNN feature extractor, specifically for the EQA baseline within the Habitat-Lab repository. They focused on the VQA (Visual Question Answering) and NAV-PACMAN baseline implementations, including data loading, model design, and training procedures. Their work involved developing and refining models, integrating them within the existing codebase, and contributing to the repository's overall machine learning capabilities for embodied AI tasks.
pythonembodied-aideep-reinforcement-learningsimulatorai-agents
mukulkhanna/generative-typography
Feb 2019 - Oct 2020
Contributions:6 commits, 5 pushes, 1 branch in 1 year 8 months
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.