Andrew Reiter
Problem Solver at Shield AI
Lincoln, Massachusetts, United States
Summary
👤
Senior🎓
Top SchoolAndrew Reiter is a multidisciplinary engineer and problem solver with 12 years of experience translating state-of-the-art robotics and autonomy research into resilient, real-world systems. He co-founded Shield AI and served as CTO, leading development of Nova, a tactically-deployed autonomous quadrotor, and later held technical and advisory roles focused on state estimation, system integration, and edge compute optimization. More recently he architected software for molten-salt nuclear reactors at ThorCon and optimized motion and compute at Boston Dynamics, demonstrating rare cross-domain fluency from embedded perception to large-scale power systems. An active contributor to performance-focused open-source projects, he improved ffmpeg efficiency and Nvidia/Jetson support in the popular Frigate NVR, showing a knack for squeezing latency and resource use out of constrained platforms. Based in Lincoln, MA, he pairs an MS in Robotics and a background in chemical engineering with hands-on leadership that moves algorithms into operational products.
11 years of coding experience
10 years of employment as a software developer
Master of Science - MS Robotics, Master of Science - MS Robotics at Harvard UniversityBachelor of Science - BS Chemical Engineering, Bachelor of Science - BS Chemical Engineering at Northwestern University
Github Skills (13)
docker10
ffmpeg10
python10
dockers10
cicd9
tensorrt9
computer-vision9
object-detection8
camera8
ai8
home-automation7
linux7
go6
Programming languages (7)
Github contributions (5)
blakeblackshear/frigate
May 2023 - Jul 2024
NVR with realtime local object detection for IP cameras
Role in this project:
Back-end & DevOps Engineer
Back-end & DevOps EngineerContributions:27 reviews, 17 PRs, 126 comments in 1 year 2 months
Contributions summary:Andrew primarily contributed to the optimization of the Frigate NVR system. Their work involved improving the efficiency of the ffmpeg processes, specifically by reducing framerate before downscaling and implementing hardware acceleration. They also refactored the code to use ffmpeg version checking and improved the compatibility for Nvidia Jetson devices, including TensorRT integration and associated Docker build configurations. Additionally, they worked on improving the iOS playback of H.265 clips.
realtimecameramqtthome-automationobject-detection
madsciencetist/teamshield
Jun 2016 - Aug 2023
Contributions:15 pushes, 1 branch, 1 comment in 7 years 3 months
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