AI / ML Principal Engineer

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.

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