# Richard Nai

> Modeling & Simulation Engineer at Anduril

Location: Santa Ana, California, United States
Profile: https://flows.cv/richardnai

## Work Experience
### Software Engineer: Modeling & Simulation @ Anduril Industries
Jan 2024 – Present | Costa Mesa, California, United States

### Simulation Engineer @ Astranis Space Technologies
Jan 2021 – Jan 2024 | San Francisco Bay Area
● Developed multiprocess Python and C++ real-time software simulation mode, enabling three teams to prototype and satellite operators to rehearse procedures without relying on limited hardware in the loop testing resources
● Enhanced sim fidelity with power modeling, solar array deployments, hardware in the loop testing, and advanced analysis tools, enabling realistic behavior, improving developer productivity, and de-risked mission dangers
● Developed Python hardware & firmware emulators to enable pure software in the loop testing by mimicking UART communication between flight hardware and firmware, reducing the need for limited hardware resources
● Created sun sensor analysis/optimization tool, reducing coverage gap 5x & explaining post-launch eclipse anomaly
● Updated flight computer solar array and photodiode drivers for emergent hardware needs
● Supported satellite flying rehearsals, and went on console for post-launch activities with subsystem engineers

### Masterarbeit & Hilfswissenschaftler (Master's Thesis & Working Student) @ Technical University of Munich
Jan 2019 – Jan 2021 | Garching, Bavaria, Germany
● Designed RSLQR feedback controller for fixed wing UAV, providing safety guarantees & better command tracking than previous PID controller
● Created state space autopilot model & simulation in Matlab/Simulink and verified performance in X-Plane flight simulator	
● Developed Python simulation environment for double deep Q reinforcement learning for UAV path planning [1]

 [1] Theile, M., Bayerlein, H., ​Nai, R​., Gesbert, D., & Caccamo, M. (2020). UAV Coverage Path Planning under Varying Power Constraints using Deep Reinforcement Learning. ​arXiv preprint arXiv:2003.02609​. [Accepted at IROS 2020]

### Graduate Nonvolatile Memory Intern @ Intel Corporation
Jan 2020 – Jan 2020
● Prototyped high pin count testing mode to enable 6x greater product verification rate for Optane​®​ product by interfacing with B6700 series memory burn in tester and updating assembly like ALPG pattern test code
● Wrote, updated and cleaned C++ and Python infrastructure to manage tester data and removed unsafe code, massively reducing product verification overhead
● Enhanced compiler warnings and fixed existing issues with verification code for the next generation line of Optane​®​ chips, ensuring future changes to the codebase would be higher quality

### Working Student @ AI Smart (Robotics & Computer Vision Startup)
Jan 2019 – Jan 2020 | Munich, Bavaria, Germany
● Programmed KAREL script to jog FANUC industrial robot, enabling demo for multi million € client
● Employed UDP socket messaging to communicate with Django Rest frontend to retrieve robot commands

### SDE Intern @ Amazon
Jan 2019 – Jan 2019 | Greater Seattle Area
● Onboarded MemCacheD to Amazon’s product advert service to increase data availability and throughput, enabling team to scale service availability effortlessly, and deployed initial cache fleet to service customers
● Engineered Java streaming service that maintained the cache, ensuring correct data for the customers

### Course Assistant @ University of Illinois Urbana-Champaign
Jan 2018 – Jan 2019 | Urbana-Champaign Area
● Tutored students in the operating systems (ECE391) and FGPA programming & digital circuitry (ECE385) courses
● Organized regular office hours and added extra late night shifts when students were struggling

### Research Assistant @ University of Illinois Urbana-Champaign
Jan 2016 – Jan 2019 | Urbana-Champaign, Illinois Area
● Developed C++/ROS emulation environment with Qt-GUI ground station, utilizing inter-process and inter-device communication over Redis and serial ports for UAV flight simulation [1]
● Programmed Catch2 and Boost unit tests and helped in design of templated C++ classes for modular autopilot [2]
● Designed aircraft tracker on pan tilt system using PID control with IMU feedback
● Implemented a wind-correction algorithm tested using hardware-in-the-loop simulation
● Assisted in the flight testing campaign of the in-house developed autopilot and aircraft

 [1] Theile, M., Dantsker, O. D., ​Nai, R​., & Caccamo, M. (2018, August). uavee: A modular, power-aware emulation environment for rapid prototyping and testing of uavs. In ​2018 IEEE 24th International Conference on Embedded and Real-Time Computing Systems
and Applications (RTCSA)​ (pp. 217-224). IEEE.
 [2] Theile, M., Dantsker, O., ​Nai, R​., Caccamo, M., & Yu, S. (2020). uavAP: A Modular Autopilot Framework for UAVs. In ​AIAA AVIATION 2020 FORUM​ (p. 3268).

### SDE Intern @ Amazon Web Services (AWS)
Jan 2018 – Jan 2018 | Seattle, Washington
● Automated Java canary service to better monitor a wider variety of AWS services, saving ~$15000/year by freeing up developer hours
● Modularized project architecture, merging similar classes for better design and cleaner code


## Education
### Master's degree in Computer Science
Technical University of Munich

### Bachelor of Science - BS in Computer Engineering
University of Illinois Urbana-Champaign


## Contact & Social
- LinkedIn: https://linkedin.com/in/richard-nai

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Source: https://flows.cv/richardnai
JSON Resume: https://flows.cv/richardnai/resume.json
Last updated: 2026-03-29