Operational Subject Matter Expert; SME

Description

The ideal candidate will be an experienced consultant with deep expertise in joint fires doctrine and modern AI-enabled targeting systems to support the CDAO's mission of accelerating digital modernization across the Department of War. The ideal candidate will possess a strong background in operational fires and demonstrated technical proficiency with advanced AI/ML platforms, including the Maven Smart System (MSS) and Edge Data Mesh (EDM) architectures.

You will work alongside experienced engineers, military operations experts, and senior leadership to support the Global Information Dominance Experiments (GIDEs).

• Provide expert consultation on joint fires doctrine, including Joint Publication 3-09, Joint Publication 3-60, and related targeting processes
• Advise on the integration of AI capabilities into joint fires kill chains and targeting cycles
• Support the development of concepts of operations (CONOPS) for AI-enabled fires coordination
• Identify and assess gaps between current joint fires processes and AI-enabled capabilities
• Lead and facilitate cross-service working groups with fires professionals across services to define requirements and develop operational use cases

• Apply knowledge of AI tools and architectures, like Maven Smart System, to improve joint fires planning and execution.
• Architect solutions leveraging commercial and custom AI tools, like Edge Data Mesh, for distributed fires data sharing and decision-making
• Design and implement data pipelines that support real-time targeting, cueing, and battle damage assessment
• Ensure AI/ML models align with and adhere to doctrinal targeting methodologies and applicable rules of engagement
• Evaluate AI system performance in joint fires contexts and recommend technical and operational improvements

• Brief senior military and civilian leadership on opportunities to integrate AI-enabled fires capabilities.
• Develop and implement roadmaps for AI-enabled joint fires capabilities
• Coordinate with combatant commands, service components, and joint task forces to align efforts with requirements.
• Prepare white papers, capability assessments, and strategic recommendations to inform decision making.
• Support acquisition and requirements development processes for emerging AI-enabled fires systems.

• 10+ years of experience in joint fires operations, targeting, or fire support coordination
• Demonstrated expertise with joint fires doctrine (JP 3-09, JP 3-60, ATP 3-09 series, or equivalent)
• 3+ years working with AI/ML systems in defense or operational contexts
• Hands‑on experience with Maven Smart System or similar AI-enabled targeting platforms
• Proven track record working with Edge Data Mesh or distributed data architectures

• Understanding of machine learning operations (MLOps) in tactical environments
• Familiarity with edge computing architectures and contested communications environments
• Knowledge of data standards for fires and targeting (e.g., VMF, Link 16, JREAP)
• Experience with AI model validation, testing, and responsible AI principles
• Comprehensive knowledge of linear regression, logistic regression, clustering, classification models, neural networks, optimization, and gradient descent.

• Military experience as Fire Support Officer, Joint Terminal Attack Controller (JTAC), Joint Fires Observer (JFO), Targeting Officer, or equivalent

• Familiarity with Joint All‑Domain Command and Control (JADC2) concepts
• Knowledge of Multi‑Domain Operations and kill web architectures
• Certifications in AI/ML, data science, or cloud architecture
• Combat deployment experience in joint or coalition operations
• Published thought leadership on AI in military operation
• Strategic thinking with ability to bridge operational and technical domains
• Excellent communication skills for diverse audiences (operators to data scientists)
• Collaborative approach to cross‑functional problem solving
• Comfort with ambiguity and rapidly evolving technologies
• Mission‑focused with understanding of warfighter needs

• Hybrid work arrangement with…