MBSE Engineer - SysML v2 - Remote

Job Description: MBSE Engineer – SysML v2 Role Type: Contractor Location: Remote About the Role We are seeking experienced MBSE Engineers specializing in SysML v2 to contribute to an AI training and evaluation project. In this role, you will leverage your systems engineering expertise to support the development and evaluation of next-generation AI systems. Your work will help improve AI models' ability to understand, interpret, and generate accurate system models through high-quality, real-world engineering input. Scope of Work Interpret and translate engineering requirements into accurate SysML v2 textual models, ensuring model fidelity and traceability. Develop reference solutions and exemplary system architectures in SysML v2 to establish benchmarks for AI evaluation tasks. Create, refine, and document evaluation rubrics to assess the quality and correctness of SysML v2 models generated by AI systems. Review, evaluate, and provide structured feedback on SysML v2 models, ensuring technical accuracy and compliance with modeling best practices. Analyze engineering requirements, Concept of Operations (ConOps), Interface Control Documents (ICDs), and architectural specifications to support modeling activities. Collaborate through written and verbal communication to clarify project deliverables and resolve technical ambiguities. Requirements Professional experience in systems engineering with hands-on expertise in SysML v2 textual modeling. In-depth understanding of requirements engineering, system architecture, interface control, and model traceability. Experience working with ConOps, ICDs, and other foundational systems engineering documents. Strong technical writing and verbal communication skills, particularly in remote and asynchronous work environments. Ability to evaluate system models and provide clear, structured technical feedback. Preferred Qualifications Experience in complex industries such as aerospace, space, defense, automotive, maritime, or communications. Familiarity with model assessment, evaluation rubric development, or model review in a systems engineering context. Previous exposure to AI, machine learning, or data-driven engineering projects.