Scientific Systems builds autonomy and AI/ML capabilities for multi-vehicle mission systems across defense and space domains. The stack is heavily C++ and Python with MATLAB, ROS, and simulation tools (MagicDock, YOLO for vision)—typical of guidance-and-control-heavy aerospace work. Active projects span hardware-in-the-loop testing, multi-vehicle autonomy simulation, and automatic target recognition, while pain points cluster around data annotation, ATR model training, and scaling from program-centric delivery into a product company. Leadership hiring (CDO, two growth roles) suggests a push to systematize deal flow and transition from bespoke systems integration.
Tech Stack 18 technologies
What Scientific Systems Is Building
◆Challenges
- Capturing large dod programs
- Transition into scalable autonomy product company
- Opportunity pipeline capture
- Win probability increase
- Collecting and annotating data
- Training atr models
- Field testing autonomous systems
- Identifying opportunity pipeline
- Growing growth team
- Meeting technical objectives of large systems programs
▲Active Projects
- Advanced m+s infrastructure for ai/autonomy testing
- Simulation of uncrewed multi-vehicle autonomy systems
- Shape and secure high-value autonomy and defense programs
- Integrate advanced autonomy, ai/ml, gnc, and optimization algorithms into complex high-fidelity simulations and hardware systems
- Lead hardware-in-the-loop and experimental testing of complex guidance, navigation, and control algorithms
- Multi-vehicle autonomy testing in operational environments
- Advanced ai/autonomy software for multi-vehicle mission systems
- Automatic target recognition development
- Atr model training
- Autonomous robotic behaviors
Hiring Activity
Department
Seniority
Notable leadership hires: Director of Growth, Growth Director, Chief Development Officer
