eGig
MBSE Engineer - SysML v2
micro1 · Remote · Posted 8h ago
This job was posted on Himalayas, an open job board. Join eGig free and you get the link to apply on Himalayas. Every job you apply to is then kept in My applications, with the pay shown up front.
Salary$55 – $120 / hr
Experience2 – 5 years
Job typeContract
Work modeRemote
Openings1
Apply by 2 Jan 2027
About the job
Role Title: MBSE Engineer - SysML v2Role 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