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$106k – $230k per year
Location
Remote (likely Albania)
Seniority
Middle · 3+ years exp
Employment
Contractor

First seen by Alion on Nov 3, 2026.

Overview
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Headquartered in Nairobi, Kenya, Recruitment Room is a global human resources technology and workforce solutions enterprise. The company provides an international employment platform encompassing Employer of Record (EOR) services, global talent acquisition, multi-currency payroll processing, contractor management, and an integrated Human Resource Information System (HRIS). By leveraging AI-powered matching tools and local legal compliance infrastructure across international markets, it enables startups, remote-first businesses, and growing enterprises to recruit, onboard, and manage cross-border teams.

MBSE Engineer - SysML v2 - Remote

Work Arrangement: Remote

Employment Type: Contract

Compensation: $105,600-$230,400/year equivalent

Hourly Rate: $55-$120/hour

Required Skills: SysML v2, Systems Engineering, Requirements Engineering, System Architecture, Model Traceability, Interface Control Documents (ICDs), ConOps, Technical Writing, Model Assessment, Rubric Creation, Model Review, Asynchronous Communication, Remote Collaboration, AI/ML Familiarity, Aerospace/Defense/Automotive Industry Background

English Proficiency: B2 or higher

Annualized compensation is an illustrative equivalent based on 40 hours/week × 48 weeks/year. Actual earnings depend on hours worked, project availability, and approved work.

About the Role

We are seeking experienced MBSE Engineers with expertise in SysML v2 textual modeling to contribute to an AI training and evaluation project. In this role, you will apply your systems engineering knowledge to help train and evaluate next-generation AI systems.

Your work will shape how AI models learn, reason, and perform through high-quality, real-world engineering input.

Key Responsibilities

  • 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 through written and verbal communication to clarify deliverables and resolve technical ambiguities.

Required 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.

  • Proficiency in technical writing and clear verbal communication, particularly in remote or asynchronous project settings.

Preferred Qualifications

  • Background in complex industries such as aerospace, space, defense, automotive, maritime, or communications.

  • Familiarity with model assessment, rubric creation, or model review in a systems engineering context.

  • Previous exposure to AI, ML, or data-driven engineering projects.

Core Skills

  • SysML v2 textual modeling

  • Systems engineering

  • Requirements engineering and traceability

  • System architecture and interface control

  • ConOps and ICD analysis

  • Reference model and exemplar architecture development

  • Model assessment, review, and validation

  • Evaluation rubric creation

  • Technical writing and structured feedback

  • Asynchronous communication and remote collaboration

  • AI/ML familiarity

Who Should Apply?

Systems engineering professionals with hands-on SysML v2 modeling experience who can develop accurate reference models, assess AI-generated outputs, create evaluation rubrics, and communicate technical findings clearly in a remote environment.

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