MBSE Engineer - SysML v2

• Interpret and translate engineering requirements into accurate SysML v2 models, emphasizing model fidelity and traceability.
• Develop reference solutions and exemplar system architectures using SysML v2 to serve as benchmarks for AI evaluation tasks.
• Use appropriate systems modeling tools to create, review, and validate SysML v2 models.
• Create, refine, and document evaluation rubrics to assess the quality and correctness of AI-generated SysML v2 models.
• Review, assess, and provide structured feedback on SysML v2 models, ensuring technical accuracy and adherence to modeling best practices.
• Analyze requirements, Concepts of Operations (ConOps), Interface Control Documents (ICDs), and architectural specifications to inform modeling efforts.
• Collaborate asynchronously through written and verbal communication to clarify project deliverables and resolve technical ambiguities.

• Professional experience in systems engineering, with hands-on expertise in SysML v1 and some exposure to SysML v2, or demonstrated experience with SysML v2 modeling.
• Ability to interpret engineering requirements and translate them into technically accurate system models.
• Understanding of model-based systems engineering principles, requirements traceability, and system architecture.
• Experience working with systems modeling tools and applying MBSE methodologies to engineering projects.

• Strong understanding of requirements engineering, system architecture, interface control, and model traceability.
• Experience using MBSE modeling tools such as Cameo Systems Modeler, MagicDraw, or other compatible systems modeling applications.
• Familiarity with SysML v2 modeling environments, including graphical or textual approaches.
• Experience working with ConOps, ICDs, and other foundational systems engineering documentation.
• Strong technical writing and communication skills, particularly in remote or asynchronous environments.
• Experience in complex engineering domains such as aerospace, space, defense, automotive, maritime, or communications.
• Familiarity with model verification, technical review, evaluation rubric development, or model quality assessment.
• Exposure to AI, machine learning, or data-driven engineering projects.

RequiredAlso useful

Also listed: remote collaboration.

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