Summary
Justin Peel is a software engineer with 14 years of experience building robust distributed systems, data pipelines, and machine learning-driven analytics, currently focused on optimizing large-scale data workflows at Google. A physicist-turned-developer, he brings deep signal processing and numerical linear algebra expertise from a PhD-level background to practical problems in IoT, camera analytics, and embedded systems. At Vivint he architected on-device ML and real-time video pipelines in Rust and Scala ecosystems, pairing embedded WebRTC/GStreamer work with cloud neural-net experiments. He has a track record of sleuthing elusive firmware and performance bugs, and of productionizing sensor-fusion and occupancy models using Kafka, Spark, and Cassandra. Quick to learn and explain complex systems, he combines research rigor with hands-on engineering to tackle hard problems end-to-end. Located in Salt Lake City, he balances low-level DSP and FPGA experience with large-scale data engineering and ML deployment.
14 years of coding experience
12 years of employment as a software developer
The University of Utah
Bachelor of Science (B.S.), Physics, 3.9, Bachelor of Science (B.S.), Physics, 3.9 at Idaho State University