About the role
CHAOS Industries is redefining modern defense with a multi-product portfolio that gives the ultimate advantage—domain dominance. The company's products are powered by Coherent Distributed Networks (CDN™), empowering warfighters, commercial air operators, and border protection teams to act faster, adapt rapidly, and stay ahead of evolving threats. CHAOS Industries was founded in 2022 and has raised a total of $1 billion in funding from leading investors, including 8VC, Accel, and Valor Equity Partners. The company is headquartered in Los Angeles, with offices in Washington, D.C., San Francisco, San Diego, Seattle, and London. For more information, please visit www.chaosinc.com .
The Role We're looking for an experienced software engineer to start Chaos's practice of using AI to better build and test our systems. The ideal candidate has used machine learning and AI to make sense of complex hardware systems - whether that's automating root cause analysis in a production process, building perception pipelines for autonomous vehicles, using LLMs to interpret sensor telemetry, or deploying anomaly detection across IoT networks. You understand that physical systems are messy, complex, and high-stakes, and you know how to build AI workflows that deliver real-world results. At CHAOS, you'll apply this skillset to cutting-edge sensing and defense systems — building AI-driven frameworks that validate, monitor, and diagnose hardware and software across radar, RF, and sensor fusion products.
What You'll Do - Build AI-driven analysis pipelines for hardware-in-the-loop test systems - automated anomaly detection, root cause analysis, and regression identification across sensor and RF data - Design and deploy LLM-powered workflows that interpret system telemetry, summarize test results, and surface actionable insights from large volumes of sensor data - Develop end-to-end automated test and validation frameworks for software, firmware, and FPGA systems using Python, integrating into CI/CD pipelines - Create intelligent monitoring and reporting dashboards that go beyond pass/fail - using ML to identify drift, degradation, and subtle failure modes in physical systems - Work closely with systems engineers, hardware engineers, and software teams to close the loop between AI-driven insights and product improvements - Help establish and scale