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Qiang lu

Senior Robotics & Embodied AI Engineer specializing in closed-loop perception-to-action systems

AmazonUniversity of DenverSanta Clara, CA9 Years ExperienceSenior LevelWorks On-Site

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About

Robotics software engineer who built the behavior-tree orchestrator for the Vulcan Stow robotic system, migrating from a state machine to significantly improve testability. Experienced with ROS 1 and Baidu Apollo workflows (rosbag, LiDAR/image extraction) from self-driving simulation work at LG Silicon Valley Lab, and currently focused on stable Docker/docker-compose-based deployments with disciplined QA and hotfix processes.

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Key Strengths

  • Designed and implemented behavior-tree-based orchestrator for a robotic stow workflow
  • Improved system testability by migrating from state machines to behavior trees
  • Led cross-team collaboration with perception and motion planning via regular syncs and rapid issue resolution
  • Methodical debugging of virtual-physical mismatch using detailed logs and controlled mock-mode service runs
  • Reduced inter-subsystem latency by deploying a low-latency kernel
  • Release stability ownership: QA feedback loop, expanding integration tests, and controlled hotfix patching

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Experience

Robotics Software EngineerAmazon Robotics · Apr 2022 – Present
Deep Learning Software EngineerHabana Labs (Intel Corporation) · Jan 2021 – Apr 2022
Senior Software EngineerLG Silicon Valley Lab (Zenith LLC.) · Sep 2018 – Jan 2021
Software Developer Intern, Artificial Intelligence (Part-time)ToolCASE LLC · Jun 2017 – Jul 2018internship

Education

University of Denverdoctorate, Electrical and Computer Engineering (2019)
Beihang Universitymaster, Astronautics Engineering (2013)
Southwest Universitybachelor, Physics (2010)

Languages

ChineseEnglish

Publications

3 publications

Autonomous intersection controlConnected and autonomous vehicles (V2X)Scenario-based testing of autonomous vehiclesAutonomous driving simulation

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Qiang luSenior Robotics & Embodied AI Engineer specializing in closed-loop perception-to-action systems