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micro1Software EngineeringSpecial Referral

MBSE Engineer - SysML v2

Remote - Worldwide

sysml v2systems engineeringrequirements engineeringsystem architecturemodel traceabilityinterface control documents (icds)conopstechnical writingmodel assessmentrubric creationmodel reviewasynchronous communicationremote collaborationai/ML familiarityaerospace/defense/automotive industry background

About the role

micro1 is engaging MBSE Engineer - SysML v2 experts to contribute to a customer's project focused on AI training and evaluation. In this role, you'll apply your expertise to help train next-generation AI systems. Your work will shape how models learn, reason, and perform through high-quality, real-world input.

Scope of Work

  • Interpret and translate engineering requirements into accurate SysML v2 textual models, emphasizing model fidelity and traceability.
  • Develop reference solutions and exemplar architectures in SysML v2 to serve as 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, assess, and provide structured feedback on SysML v2 models, ensuring technical accuracy and adherence to modeling best practices.
  • Analyze requirements, ConOps, interface control documents (ICDs), and architectural specifications to inform modeling efforts.
  • Collaborate asynchronously via written and verbal communication to clarify project deliverables and resolve technical ambiguities.

Preferred Qualifications

  • Minimum 3 years of professional experience in systems engineering with demonstrated 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 artifacts.
  • Proficient in technical writing and clear verbal communication, especially in remote or asynchronous project settings.
  • Background in complex industries such as aerospace, space, defense, automotive, maritime, or communications is beneficial but not required.
  • Familiarity with model assessment, rubric creation, or model review in a systems engineering context is a plus.
  • Previous exposure to AI, ML, or data-driven engineering projects is advantageous

Benefits

Not provided in the source listing.

Where you can work

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Remote does not always mean work from any country. Always check the employer’s location and working-hour requirements.

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