Mission data processing and sensor systems for defense and space
SciTec builds real-time sensor data processing systems for U.S. defense and space operations, with a tech stack anchored in Java, Python, C++, Kubernetes, and AWS—indicating a cloud-native, containerized architecture designed for high-availability mission systems. Active hiring is heavily skewed toward senior engineers (17 of 42 open roles) and staff-level technicians, reflecting deep technical problem-solving in low-latency signal processing and algorithm development rather than volume hiring. The organization is grappling with integrating security into development workflows within a DCAA-regulated environment, a constraint that shapes both tooling choices (SonarQube, Keycloak, SCAP compliance scanning) and organizational pain.
SciTec, a subsidiary of Firefly Aerospace, develops sensor data processing and mission intelligence systems for U.S. and allied military and space agencies. The company operates across three interconnected domains: advanced signal processing (OPIR, missile warning), real-time data pipeline architecture, and secure software delivery in a defense-contracting environment. With 201–500 employees based in Princeton, New Jersey, SciTec integrates sensor physics, algorithm development, and modern software engineering to transform raw sensor data into actionable intelligence. The organization emphasizes government-owned, open-architecture software to reduce vendor lock-in and accelerate integration across multi-domain operations.
Java, Python, C++, Kubernetes, Docker, AWS EKS, GitLab CI/CD, Prometheus, Grafana, and Red Hat Enterprise Linux form the core. Security and compliance tooling include SonarQube, Keycloak, and SCAP scanning for DCAA audit requirements.
OPIR sensor data processing, next-generation missile warning software, low-latency signal processing pipelines, advanced algorithm development, and secure DevSecOps integration for regulated defense environments.
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SciTec'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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