# Applied AI Engineer at Chaos

- Company: Chaos
- What the company does: CHAOS Industries builds a multi-product portfolio powered by Coherent Distributed Networks (CDN TM), giving military, commercial, and border teams the ultimate advantage: domain dominance. Backed by Accel and NEA.
- Company website: https://chaosinc.com/
- Type: Startups (AI role)
- Level: Mid level
- Location: San Francisco, California, United States
- Work setup: On-site
- Pay: $150K to $170K base salary per year (USD)
- Posted: 2026-09-14
- Apply by: 2026-10-29
- Apply: https://job-boards.greenhouse.io/chaosindustries/jobs/5237013007
- Page: https://www.1752.vc/careers/jobs/chaos-applied-ai-engineer/

## About the role

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.

## What they're looking for

- 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

Tags: Engineering