We are seeking a skilled engineer to lead the modeling and simulation efforts for Camgian. The modeling and simulation lead will play a critical role in not only in the test and evaluation of Camgian's diverse product set but also integrating model driven engineering into the full development lifecycle to reduce risk and increase development velocity. They will be responsible for directly developing and validating models at various levels of complexity as well as advising and overseeing their development within other functional areas. This position will involve coordination with a diverse set of stakeholders and will require excellent written and verbal communication skills.
Qualifications
- Bachelor's degree in engineering, math, or related technical field with at least 15+ years of applicable experience.
- Strong foundation in mathematics
- Experience with defense simulation systems
- Experience with AI/ML model training and integration for simulating behaviors
- Experience with user/human factor models
- Experience in combat system modeling
- Experience modeling distributed multi agent systems
- Strong ability to mitigate technical risks
- Proficiency with multiple languages and modeling frameworks including C++ and Python
- United States Citizenship
Desired Skills
- Experience with Model Based Engineering tools (Enterprise Architect, Cameo, etc.)
- Experience with graphical modeling languages like UML, SysML, and defense frameworks
- Experience with simulation standards like DIS and HLA
- Experience with discrete event models
- Domain experience in combat systems and sensor fusion
- Domain experience in AI/ML, data science, and prognostics
- Master's or PhD in related field
Key Responsibilities
- Define simulation needs across the board from ML model training through mission effectiveness
- The creation of models across several levels of abstraction
- Perform operational and mission effectiveness analysis
- Verification and validation of existing models
- Simulation result analysis
- Assist in risk management and identify approaches for technical risk burndown
