Defense AI and signal processing for real-time field operations
Expedition Technology builds machine learning and signal processing systems for the defense and intelligence sectors. The stack—Python, Kubernetes, AWS, React, and GraphQL—supports both backend ML model development and web-based mission interfaces. Current project focus spans real-time algorithm deployment, training data infrastructure, and workflow automation; pain points cluster around compliance (NIST 800-53, federal contracts) and the hard engineering problem of translating lab models into field-ready systems operating at scale.
Expedition Technology is a privately held, employee-owned defense contractor headquartered in Northern Virginia. The company serves U.S. defense and intelligence agencies with software solutions centered on machine learning, signal processing, and computer vision. Their product surface includes a training data storefront, mission workflow services, verification and validation systems, and real-time applications for electronic warfare and RF geolocation. Operations span cloud infrastructure on AWS and GovCloud, with active compliance management for federal acquisition regulations and NIST standards. The organization is 51–200 employees and actively hiring, with engineering representing the dominant headcount.
Core: Python, Docker, Kubernetes, AWS (Lambda, Fargate, RDS, DynamoDB). Frontend: React, Vue, Angular, Next.js, TypeScript, Tailwind CSS. Infrastructure: GitLab, CloudFormation, IAM. Middleware: GraphQL, React Query, OpenLayers.
Real-time ML and signal processing for defense: mission workflow services, training data infrastructure, electronic warfare DSP systems, RF geolocation models, and deployment automation. Heavy focus on transitioning lab models into field-ready applications.
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Expedition Technology's technology stack, projects, and hiring signals are inferred from public hiring and company data — career pages, public listings, and company web presence — then clustered and de-duplicated. Figures are estimates that refresh over time. Read our full methodology →
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