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CHAOS Industries

Develops military sensor and radar systems

AI Engineer - Physical Systems and Sensing

Applied Machine LearningConfirmed live in the last 24 hours
Full-timeEl Segundo, CA, USAIn-person

About the Job

Requirements

  • At least 5 years of experience applying artificial intelligence or machine learning to physical or Internet of Things systems, such as manufacturing analytics, autonomous vehicles, robotics, sensor systems, or industrial automation.
  • Strong Python proficiency and hands-on experience building production data pipelines and machine-learning workflows.
  • Experience working with real-world sensor data, including time-series analysis, signal processing, or anomaly detection.
  • Familiarity with large language models and artificial intelligence tooling in engineering workflows, such as log analysis, code generation, or root-cause investigation.
  • Background working with hardware-in-the-loop systems, embedded systems, or real-time data streams.
  • Continuous integration and continuous delivery experience with tools such as Jenkins, Azure, or Bitbucket, and comfort with Dockerized or cloud-deployed environments.
  • Ability to work cross-functionally with hardware and systems engineering teams, read schematics, understand test racks, and communicate with radio-frequency engineers.

Responsibilities

  • Build artificial-intelligence-driven analysis pipelines for hardware-in-the-loop test systems, including automated anomaly detection, root-cause analysis, and regression identification across sensor and radio-frequency data.
  • Design and deploy large-language-model-powered workflows to 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 field-programmable gate array systems using Python, integrating them into continuous integration and continuous delivery pipelines.
  • Create intelligent monitoring and reporting dashboards that use machine learning to identify drift, degradation, and subtle failure modes in physical systems.
  • Work with systems engineers, hardware engineers, and software teams to connect artificial-intelligence-driven insights with product improvements.
  • Help establish and scale the team's testing and validation capabilities as a foundational member of the group.

Desired Qualifications

  • Defense or aerospace domain experience, including radar, radio-frequency/in-phase and quadrature data processing, or sensor fusion.
  • Experience with CUDA, field-programmable gate array workflows, or GPU-accelerated computing.
  • Background in autonomous systems, self-driving vehicles, or robotics perception pipelines.
  • Experience with performance testing, fault injection, or simulation-based validation.
  • Experience scaling artificial-intelligence or machine-learning systems from prototype to production in regulated environments.

About the Company

CHAOS Industries

Develops military sensor and radar systems

Chaos Industries builds defense technology by creating sensor, radar, and network systems for military and security use. Its Coherent Distributed Networks (CDN) synchronizes a network of sensors to improve detection and situational awareness across land, sea, and air, enabling earlier threat warnings. Its product line includes Vanquish multistatic radar for early warning and tracking of unmanned systems and missiles, the HYDRA software-defined platform for detection and communications, and ASTRIA for long-range mobile multi-object tracking; it has formed an Aerospace Division after acquiring Ziva Corporation and Atropos Group. The company targets U.S. and allied defense clients, focusing on software-driven systems that can be updated and deployed quickly, with the goal of delivering faster reactions and better national security readiness while expanding production and advancing its technology roadmap.

Company size
201-500
Company stage
Series D
Total funding
$1B
Headquarters
Los Angeles, CA
Founded
2022

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