Software Engineer (Applied AI)
Role summary
CHAOS is seeking a motivated Applied AI Engineer to develop, integrate, and deploy AI/ML capabilities across product lines, focusing on defense applications. This role involves conducting advanced AI research, building applied AI systems with model integration and inference services, and creating production-quality software for data pipelines and model serving. The engineer will evaluate and improve model performance in real-world conditions, contribute to AI system reliability through testing and analysis, and manage time effectively across various technical tasks. A strong foundation in data science and software engineering, with at least 2 years of professional development experience and Python skills, is required. Experience with AI/ML model integration, serving, lifecycle management, evaluation, and production systems is essential. Familiarity with APIs, distributed systems, containers, CI/CD, observability, and edge deployment is preferred.
- 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
- 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
### Benefits
- Generous pre-IPO stock option grants, relocation assistance + (coming soon!) annual bonuses
- Free daily lunch, ‘No meeting Fridays’, unlimited PTO (for exempt employees), casual dress code
- Life, FSA, HSA, 401k (+ Company match), and more
- Medical, dental and vision benefits will be 100% paid for by the company- 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
- Excited to learn unfamiliar technical domains
- Experience with AI/ML model integration, model serving infrastructure, or model lifecycle management
- Experience with model evaluation, benchmarking, robustness testing, interpretability, or ML observability
- Experience building, testing, deploying, and supporting production software systems
- Familiarity with APIs, distributed systems, containers, CI/CD, observability, and edge deployment environments/GPU optimization
- Experience with digital picture or video processing
- Experience with analog or digital RF signal processing
- Experience productizing machine learning models, LLM applications, computer vision systems, signal-processing systems, or autonomous/robotic systems
- Experience with drone, robotics, sensor fusion, tracking, or moving-target detection systems
- Experience building AI agents or agentic workflows
- 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
