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micro1 / Software

MBSE Engineer - SysML v2

nuevaDesde Argentina

  • Remoto desde Argentina
  • Contrato
  • USD 55 – 120/h
  • Remoto
  • Semi senior
  • 3+ años de experiencia

Requisitos

  • • 3+ años de experiencia
  • • inglés intermedio o más (el aviso está escrito en inglés)

Suma si tenés

  • machine learning
  • inteligencia artificial

La búsqueda

Role Title: MBSE Engineer - SysML v2

Role Type: Contractor

Location: Remote 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

Originally posted on Himalayas

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