Systems Engineer | Remote

Crossing Hurdles; New Zealand; Remote; NZD 90,000 - 130,000; Full time

Job summary

Crossing Hurdles in New Zealand seeks a systems engineer to interpret engineering requirements and translate them into accurate SysML v2 models, emphasizing fidelity and traceability. You will develop reference solutions and exemplar architectures to benchmark AI evaluation tasks within MBSE contexts.

Using Cameo Systems Modeler, MagicDraw, or similar tools, you will create, review, and validate models; refine evaluation rubrics; and provide structured feedback.

Qualifications

• Hands-on MBSE with SysML v1 or v2.
• Ability to translate requirements into system models.
• Understanding MBSE principles and traceability.
• Experience with SysML tooling and project documentation.
• Excellent English written and verbal communication.

Responsibilities

• Interpret and translate engineering requirements into accurate SysML v2 models, emphasizing fidelity and traceability.
• Develop reference solutions and exemplar system architectures using SysML v2 to benchmark AI evaluation tasks.
• Use appropriate systems modeling tools to create, review, and validate SysML v2 models.
• Create, refine, and document evaluation rubrics to assess AI-generated SysML v2 models.
• Review, assess, and provide structured feedback on SysML v2 models, ensuring technical accuracy and adherence to modeling best practices.
• Analyze requirements, ConOps, ICDs, and architectural specifications to inform modeling efforts.
• Collaborate asynchronously through written and verbal communication to clarify project deliverables and resolve technical ambiguities.

Skills

SysML v1

SysML v2

MBSE

Requirements traceability

Systems engineering

Technical writing

Remote communication

Education

Bachelor's degree in Systems Engineering

Tools

Cameo Systems Modeler

MagicDraw

Job description

• Interpret and translate engineering requirements into accurate SysML v2 models, emphasizing model fidelity and traceability.
• Develop reference solutions and exemplar system architectures using SysML v2 to serve as benchmarks for AI evaluation tasks.
• Use appropriate systems modeling tools to create, review, and validate SysML v2 models.
• Create, refine, and document evaluation rubrics to assess the quality and correctness of AI-generated SysML v2 models.
• Review, assess, and provide structured feedback on SysML v2 models, ensuring technical accuracy and adherence to modeling best practices.
• Analyze requirements, Concepts of Operations (ConOps), Interface Control Documents (ICDs), and architectural specifications to inform modeling efforts.
• Collaborate asynchronously 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 v1 and some exposure to SysML v2, or demonstrated experience with SysML v2 modeling.
• Ability to interpret engineering requirements and translate them into technically accurate system models.
• Understanding of model-based systems engineering principles, requirements traceability, and system architecture.
• Experience working with systems modeling tools and applying MBSE methodologies to engineering projects.
• Strong understanding of requirements engineering, system architecture, interface control, and model traceability.
• Experience using MBSE modeling tools such as Cameo Systems Modeler, MagicDraw, or other compatible systems modeling applications.
• Familiarity with SysML v2 modeling environments, including graphical or textual approaches.
• Experience working with ConOps, ICDs, and other foundational systems engineering documentation.
• Strong technical writing and communication skills, particularly in remote or asynchronous environments.
• Experience in complex engineering domains such as aerospace, space, defense, automotive, maritime, or communications.
• Familiarity with model verification, technical review, evaluation rubric development, or model quality assessment.
• Exposure to AI, machine learning, or data-driven engineering projects.