MBSE Systems Engineer (SysML / Cameo)

Remote (Remote)

Job Title: MBSE Systems Engineer (SysML / Cameo)

Location: Remote

Clearance: Top Secret

Years of experience: Ranging from 3 years to 15 years (depends on LCAT)

Education: At minimum a Bachelor's Degree, higher LCATS require a Master's and PHD.

(Part of Proposal Contract, anticipated work to start in August. We are actively looking for candidates interested in being part of the proposal process and helping shape the team prior to award.)

This effort supports the modernization of Model-Based Systems Engineering (MBSE) capabilities within a secure, cloud-enabled environment. The focus is on advancing system modeling, improving interoperability, and integrating digital engineering practices across complex system-of-systems environments.

The work includes both:

• Sustainment and enhancement of existing MBSE environments

• Research and development of next-generation SysML-based modeling approaches

Engineers will contribute to the transition from SysML 1.x to SysML 2.x, support integration with modeling and simulation ecosystems, and enable deployment of scalable, cloud-native solutions through CI/CD pipelines.

• Design, develop, and maintain SysML models, including system and system-of-systems architectures

• Support migration efforts from SysML 1.x to SysML 2.x, ensuring reuse and minimal disruption

• Develop and refine SysML reference models and modeling standards

• Utilize tools such as Cameo Systems Modeler for model development and management

• Support deployment, integration, and troubleshooting of MBSE tools within secure environments

• Evaluate and prototype next-generation modeling tools and workflows

• Integrate SysML models with Modeling & Simulation (M&S) ecosystems

• Support validation of system models within simulation environments (e.g., AFSIM)

• Develop interfaces between models, data sources, and analytic tools

• Integrate MBSE practices into broader analytic and digital engineering workflows

• Support system interoperability between models, databases, and analytics platforms

• Contribute to scalable, cloud-native architecture and CI/CD-driven deployments