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# Software Engineer, Applied AI

- **Company:** CHAOS Industries
- **Location:** San Francisco, CA, United States
- **Experience:** Experienced
- **Salary:** $150,000.0 - $170,000.0
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Computer Vision, Continuous Integration, Distributed Systems, Global Positioning Systems (GPS), Python (Programming Language), Machine Learning, Radio-Frequency Identification, Performance Tuning, Rapid Prototyping Process, Sensor Fusion, Signal Processing, Software Engineering, Systems Integration, Video Editing, Data Processing, Large Language Models, Model Validation, Information Technology, Low Latency, Machine Learning Operations, Data Pipelines
- **Published:** July 28, 2026
- **Apply:** https://www.dice.com/job-detail/c8414eac-dd13-4c21-adfd-8d328359f078

## About the Role

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., * 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, * 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 autonomorobotic 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.

## Description

* 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.


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