AI Engineer, Physical Systems and Sensing

CHAOS Industries•El Segundo, CA

8d•$145,000 - $250,000•Onsite

About The Position

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. 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.

Requirements

5+ years of experience applying AI/ML to physical or IoT systems - e.g., manufacturing analytics, autonomous vehicles, robotics, sensor systems, industrial automation, or similar domains
Strong Python proficiency with hands-on experience building data pipelines and ML workflows (not just Jupyter notebooks — production systems)
Experience with real-world sensor data: time-series analysis, signal processing, anomaly detection, or similar
Familiarity with LLMs and AI tooling in engineering workflows (e.g., using LLMs for log analysis, code generation, root cause investigation)
Background working with hardware-in-the-loop systems, embedded systems, or real-time data streams
CI/CD experience (Jenkins, Azure, Bitbucket) and comfort with Dockerized/cloud-deployed environments
Ability to work cross-functionally with hardware and systems engineering teams — you can read a schematic, understand a test rack, and talk to an RF engineer

Nice To Haves

Defense or aerospace domain experience (radar, RF/IQ data processing, sensor fusion)
Experience with CUDA, FPGA workflows, or GPU-accelerated computing
Background in autonomous systems, self-driving vehicles, or robotics perception pipelines
Performance testing, fault injection, or simulation-based validation
Experience scaling AI/ML systems from prototype to production in regulated environments

Responsibilities

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 the team's testing and validation capabilities as a foundational member of the group
Work on-site in El Segundo, CA