# Dongran Liu

> Building End-to-End VLA Solutions on the Edge for Physical AI, Principal Software Engineer

Location: Sunnyvale, California, United States
Profile: https://flows.cv/dongran

VLA, LLM, TensorRT, Model Deployment, Inference Optimization, Machine Learning, Training infra, GPU Acceleration, Computer Vision, Robotics, Tracking

Dedicated to developing lightning fast host and device algorithms for real-time artificial intelligent systems

## Work Experience
### Principal Software Engineer @ NVIDIA
Jan 2022 – Present | Redmond, Washington, United States
Developing NVIDIA’s End-to-End VLA Solution for Autonomous Driving and Deploying on NVIDIA Chips

### Senior Software Engineer, Perception @ Waymo
Jan 2019 – Jan 2022 | Mountain View, California, United States
Formerly the Google self-driving car project.
Build a perception pipeline to make the car see and understand the world.

### Software Engineer @ Argo AI
Jan 2017 – Jan 2019 | Palo Alto
Developed and deployed the latest advancements in artificial intelligence, computer vision and machine learning to help build safe and efficient self-driving vehicles.
Dedicated to system design, implementation, integration, performance improvements and onboard validation of GPU and other type of acceleration systems available on vehicle.
Worked on algorithms such as deep learning inference libraries, fast geometry computations, system latency diagnostics, sensor data visualization and efficient utilities.

### Research Engineer @ Ford Motor Company
Jan 2015 – Jan 2017 | Palo Alto
Develop computer vision, sensor fusion and machine learning algorithms to support the products of Autonomous Vehicle (AV) and Driver Assist Technologies (DAT).
Implement, validate and test the algorithm on a vehicle and perform benchmarking.
Working on multi-sensor fusion projects involving LIDAR, Camera, Radar, Ultrasonic, etc.

### Software Engineer Intern @ Honda Research Institute USA, Inc.
Jan 2015 – Jan 2015 | Mountain View
Implemented LIDAR and RADAR data fusion to achieve the model based vehicle detection and tracking for autonomous urban driving.

Designed and programmed the algorithm in C++ using dynamic Bayesian Network and Particle filter to detect and track the vehicle.

Integrated the whole system into the Real-time system RTMaps and analyzed the data in real time.

### Research Assistant @ Computer Vision and Robotics Research Laboratory
Jan 2014 – Jan 2014 | University of California, San Diego
Designed and programmed the traffic sign learning based detection and recognition using Integrated Channel Features and Aggregated Channel Features with modified AdaBoost classifier.

Realized 30fps real-time detection system. Achieved traffic sign Kalman-Filter tracking and semi-automatic active learning.

Extended the LISA Traffic Sign Dataset, which is the largest public dataset of US traffic signs.


## Education
### Master of Science (M.S.) in Electrical and Electronics Engineering
UC San Diego

### Bachelor of Science (B.S.) in Electrical and Electronics Engineering
Miami University

### Bachelor of Science - BS in Computer Science
Dalian University of Technology


## Contact & Social
- LinkedIn: https://linkedin.com/in/dongran-liu-b1b46455

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Source: https://flows.cv/dongran
JSON Resume: https://flows.cv/dongran/resume.json
Last updated: 2026-04-12