Software Engineer, CHAOS Industries

• 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

Desirable

• 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