AI / ML Principal Engineer with Security Clearance

AI/ML Principal Engineer The AI/ML Principal Engineer partners with data scientists, ML engineers, and intelligence analysts to design, evaluate, and integrate advanced AI/ML capabilities across Military Operational Systems and advance NextGen products and services. This role shapes technical direction, supports government stakeholders, and helps transition cutting‑edge models and analytics into operational environments.

Location: Aberdeen, MD (Hybrid; ~75% onsite; 10–15% travel) Clearance: Top Secret (with ability to obtain SCI) preferred. Highly qualified Secret-cleared candidates may also be considered.

Responsibilities: • Deliver AI/ML-focused systems engineering expertise to validate technical and operational solutions. • Design and develop AI/ML-based solutions for defense, intelligence, and mission applications. • Contribute AI/ML engineering inputs to acquisition documents (SOWs, specifications, engineering plans, evaluation strategies). • Prepare analysis products and strategic recommendations aligned to government objectives. • Diagnose and resolve AI/ML system challenges to ensure reliability and mission readiness. • Define problem spaces, lead studies, and supervise analytical data collection to support decision-making. • Provide guidance and consultation to cross-functional personnel. • Assist in implementing modern AI/ML frameworks, tools, and software development practices. • Support development and assessment of research and SBIR topics, BOMs, RFPs, and related artifacts. • Communicate progress clearly to leadership and government stakeholders.

Required Qualifications: • BS in Computer Science or related field; MS/PhD preferred. • 8+ years of relevant experience in AI/ML, applied analytics, or systems engineering. • Demonstrated ability to rapidly learn emerging technologies in evolving mission domains. • Strong problem-solving and analytical skills with the ability to interpret complex, diverse data sets. • Excellent communication and collaboration skills across multidisciplinary teams. • Experience building, optimizing, and maintaining large-scale distributed data pipelines. • Familiarity with a range of ML models and intelligence-analytic use cases. • Understanding of AI/ML system performance factors such as model validation, statistical analysis, and operational constraints. • Foundational knowledge of the intelligence cycle and intelligence data production workflows. • Proficiency in modern algorithms, data science methods, and systems/network security fundamentals.

Desired Qualifications/Experience: Defense / Intelligence / Federal Experience • Experience supporting U.S. Army organizations (e.g., DEVCOM, C5ISR Center, INSCOM, CPE ISW, CPE C2IN, etc.) or similar Military or Intelligence organizations. • Background in DoD or Federal RDT&&E environments or government labs. • Familiarity with EW, SIGINT, Cyber, or multi-domain operations/systems.

Mission-Aligned AI/ML Experience • Experience applying AI/ML to operational military use cases such as: ◦ Tactical edge AI/ML model deployment on constrained compute environments. ◦ RF analytics, blind signal detection, electronic support/attack workflows, or sensor-tasking automation. ◦ Integrating AI/ML agents with EW/SIGINT payloads, software-defined radios, or vehicle-mounted systems. • Experience developing or integrating models in C5ISR domains—including all-source analytics, cyber intelligence, electronic warfare, signals intelligence, PED, weather analytics, or data fusion. • Familiarity with containerized AI/ML deployment (e.g., GPU-accelerated pipelines, DevSecOps, CI/CD environments) aligned to enterprise architectures. • Background supporting system-of-systems engineering, rapid prototyping, or “quick reaction” capability development for government customers.

Technical && Architectural • Understanding of MOSA principles (e.g., CMOSS, VICTORY, MORA) and digital engineering/MBSE practices. • Experience designing or integrating distributed analytics with Army (or similar) cloud or hybrid-edge environments. • Proficiency with data taxonomies, interoperability standards, and mission-data synchronization across tactical and enterprise systems.