AI Engineer (Physical Systems and Sensing)

Join CHAOS, a cutting-edge company focused on sensing and defense systems. As an AI Engineer, you will build AI-driven frameworks for hardware and software validation, monitoring, and diagnosis. You will work closely with systems engineers, hardware engineers, and software teams to improve product performance. This position is based in El Segundo, CA, and offers generous benefits, including pre-IPO stock options, relocation assistance, and unlimited PTO.

• Appliquer des compétences en intelligence artificielle pour développer des systèmes de détection et de défense.
• Construire des pipelines d'analyse pilotés par l'IA pour les systèmes de test matériels.
• Collaborer avec des ingénieurs systèmes, des ingénieurs matériels et des équipes logicielles pour améliorer les produits.
• You understand that physical systems are messy, complex, and high-stakes, and you know how to build AI workflows that deliver real-world results
• 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
• 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
• Familiarity with LLMs and AI tooling in engineering workflows (e.g., using LLMs for log analysis, code generation, root cause investigation)
• Experience with real-world sensor data: time-series analysis, signal processing, anomaly detection, or similar
• 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
• 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)
• 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