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AI/ML Principal Engineer

AberdeenHybridFull-time

AI Summary

Designs, evaluates, and integrates AI/ML capabilities for military operational systems, shaping technical direction and transitioning models into operational environments.

About this role

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.

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.

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.

·Top Secret Clearance (with ability to obtain SCI) preferred. Highly qualified Secret-cleared candidates may also be considered.

Desired Qualifications

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:

o Tactical edge AI/ML model deployment on constrained compute environments.

o RF analytics, blind signal detection, electronic support/attack workflows, or sensor-tasking automation.

o 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.

Skills

Acquisition DocumentsAI/ML FrameworksAll-source AnalyticsAnalytical SkillsApplied AnalyticsAWSAzureBlind Signal DetectionC5ISRC5ISR CenterCI/CDCloud ArchitecturesCMOSSComputer VisionCPE C2INCPE ISWCross-Functional CollaborationCyberData FusionData PipelinesData TaxonomiesDeep LearningDefense SystemsDEVCOMDevSecOpsDigital EngineeringDistributed AnalyticsDockerDoD RDT&EEdge AIElectronic AttackElectronic SupportEngineering PlansEnterprise ArchitecturesEvaluation StrategiesEWFederal LabsGCPGitGovernment Stakeholder CommunicationGPU-accelerated PipelinesHybrid-Edge ComputingINSCOMIntelligence AnalysisIntelligence CycleIntelligence Data ProductionInteroperability StandardsKubernetesLarge-scale Data PipelinesMachine LearningMBSEMilitary Operational SystemsMission-data SynchronizationMLOpsModel OptimizationModel ValidationMORAMOSAMulti-domain OperationsNLPProblem-solvingPythonPyTorchQuick Reaction Capability DevelopmentRapid PrototypingReinforcement LearningResearch And DevelopmentRF AnalyticsRFP DevelopmentSBIRSCI EligibilitySecret ClearanceSensor Tasking AutomationSIGINTSignal ProcessingSoftware-defined RadiosSoftware Development PracticesSOW AuthoringSparkSQLStatistical AnalysisSystem-of-systems EngineeringSystems EngineeringTactical Edge DeploymentTechnical DirectionTensorFlowTop Secret ClearanceTransfer LearningU.S. Army OrganizationsVictory

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