Wenjie Qiu
Applied Scientist at Amazon
Bellevue, Washington, United States
Summary
👤
Senior🎓
Top SchoolWenjie Qiu is an Applied Scientist at Amazon with a decade of experience focused on large language models, reinforcement learning, and AI agents, blending industrial impact with fresh academic rigor from a PhD in Computer Science at Rutgers. His doctoral work on programmatic reinforcement learning for interpretable policies informs practical solutions that bridge LLMs and RL, and his internship stints at LinkedIn and Samsung Research America expanded that portfolio into embodied AI and agent systems. Wenjie has a track record of shipping research-driven features through Amazon internships and now full-time work, and he pairs deep technical breadth with an interest in neurosymbolic and program synthesis approaches. Fluent in Chinese and English, he brings both global perspective and a taste for creative expression—as hinted by his evocative GitHub bio—while pursuing AI that is both auditable and production-ready.
10 years of coding experience
High School Diploma, High School Diploma at Nanya Middle SchoolBachelor of Engineering - BE, Electronic Information Engineering, Bachelor of Engineering - BE, Electronic Information Engineering at Central China Normal UniversityMaster of Science - MS, Computer Science, Master of Science - MS, Computer Science at Washington University in St. LouisDoctor of Philosophy - PhD, Computer Science, Doctor of Philosophy - PhD, Computer Science at Rutgers University
Chinese, English
Github Skills (46)
robotics10
toolbox10
image-processing10
stable-baselines10
computer-vision10
reinforcement-learning9
pytorch9
python9
opencv9
robustness9
deep-learning9
gaussian9
interaction9
deep-reinforcement-learning9
physics-engine8
Programming languages (4)
Github contributions (5)
Roadsong/rutgers-cs211-recitations
Sep 2020 - Nov 2020
Contributions:23 commits, 22 pushes, 1 branch in 2 months
RU-Automated-Reasoning-Group/GCRL-LTL
Aug 2023 - Feb 2024
NeurlPS'23 Instructing Goal-Conditioned Agents with LTL Objectives
Contributions:8 PRs, 71 pushes, 3 branches in 6 months
reinforcement-learning
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