Software Engineer, Applied AI

CHAOS Industries•San Francisco, CA

1d•$150,000 - $170,000•Hybrid

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. Role Overview CHAOS is seeking a highly motivated, mission-oriented Applied AI Engineer to help develop, integrate, and deploy AI/ML-powered capabilities across our product lines. In this role, you will work closely with other data scientists, software engineers, product teams, and mission stakeholders to conduct advanced AI research and turn them into reliable, real-world software. The work will focus especially on defense applications where the systems must perform with extreme accuracy under constrained, adversarial, and operationally complex conditions. We are looking for someone with strong data science and software engineering fundamentals, a record of technical excellence, and demonstrated experience applying AI/ML techniques to real products. Experience in aerospace, defense, critical infrastructure, robotics, RF systems, or other highly regulated or mission-driven environments is a strong plus. You should be comfortable operating with independence, learning unfamiliar technical domains quickly, working across disparate teams, and moving prototypes toward production with limited oversight.

Requirements

BS/MS in Computer Science, Engineering, Machine Learning, Applied Mathematics, Physics, or a related technical field.
2+ years of professional software development experience.
Strong Python programming skills.
Experience building, testing, deploying, and supporting production software systems.
Experience with AI/ML model integration, model serving infrastructure, or model lifecycle management.
Experience with model evaluation, benchmarking, robustness testing, interpretability, or ML observability.
Familiarity with APIs, distributed systems, containers, CI/CD, observability, and edge deployment environments/GPU optimization
Excited to learn unfamiliar technical domains

Nice To Haves

Experience with analog or digital RF signal processing
Experience with digital picture or video processing
Experience productizing machine learning models, LLM applications, computer vision systems, signal-processing systems, or autonomous/robotic systems.
Experience building AI agents or agentic workflows
Experience with drone, robotics, sensor fusion, tracking, or moving-target detection systems.
Contributions to open-source projects or technically significant public work.
Experience in aerospace, defense, critical infrastructure, robotics, or other mission-critical environments is a strong plus
Familiarity with edge deployment, low-latency inference, hardware-constrained systems, or degraded-connectivity environments.

Responsibilities

Build applied AI systems across CHAOS product lines, including model integration, inference services, evaluation pipelines, and production-facing AI capabilities
Perform research and build products by working with product and mission teams to research, collect data, verify hypothesis and create robust, testable, maintainable, and deployable models
Evaluate and improve model performance under real-world conditions, including adversarial GPS denied environments, low-power or edge deployments, and degraded or noisy inputs
Develop production-quality software for data pipelines for acquisition, model serving, monitoring, lifecycle management, data processing, and system integration.
Create rapid prototypes with mission and product teams, other relevant stakeholders and iterate toward production-ready implementations.
Contribute to AI system reliability, by conducting testing, benchmarking, observability, interpretability, failure analysis, and performance optimization.
Learn quickly from existing codebases, documentation, research artifacts, and domain experts, then use that knowledge to drive execution.
Manage time effectively across meetings, technical discovery, implementation, experimentation, and production support.