Model Based Systems Engineer (Expert)
KBR
- Location
- Chantilly, Virginia
- Work model
- On-Site
- Level
- Staff
- Salary
- $150k – $187k/yr
- Posted
- Sep 8, 2026
Skills
About this role
Title: Model Based Systems Engineer (Expert) Belong. Connect. Grow. with KBR! KBR’s National Security Solutions team provides high-end engineering and advanced technology solutions to our customers in the intelligence and national security communities. In this position, your work will have a profound impact on the country’s most critical role – protecting our national security. KBR is seeking a Digital Engineering Technical Modeler to join our team on site in Chantilly, VA. An active TS/SCI with poly is required. Why Join Us? Innovative Projects: KBR’s work is at the forefront of engineering, logistics, operations, science, program management, mission IT and cybersecurity solutions. Collaborative Environment : Be part of a dynamic team that thrives on collaboration and innovation, fostering a supportive and intellectually stimulating workplace. Impactful Work: Your contributions will be pivotal in designing and optimizing defense systems that ensure national security and shape the future of space defense. This effort is in direct support of our IC Customer in the Chantilly VA area. We are looking for the self-driven, the bold, the passionate Systems Engineers who will join KBR on an exciting journey of professional thrill and personal growth as the Team pursues and steps up to strategic Customer Enterprise-level challenges! This position provides hands-on leadership and guidance within and across Program Systems Engineering (SE) Integrated Team to deliver 100% on-target success of both top-down DE Culture Transformation objectives and hands-on DE taskings. Domain knowledge and executional focus areas include Space and Ground SE and Lifecycle Management fundamentals; DE including MBSE foundational principles including processes and standards; Space and Ground mission, systems, and lifecycle development phase-tailored applications, tools, techniques of DE/MBSE to deliver and maximize Enterprise-wide efficiency, interconnectivity, and re-use within DE-centric mission architecture, design data, and organizational knowledge management systems.
Key Responsibilities
Deliver foundational digital engineering (DE) approaches that ensure the engineering and MS&A processes are met. Provides technical advice and guidance to senior managers regarding the creation and implementation of new MBSE methods and techniques. Develop and deliver enterprise level digital engineering approaches, and ensure that the models, data, and information to support the development of space and intelligence architecture, systems, and other acquisitions are accessible, interconnected, and structured in such a way to ensure organizational goals are met or exceeded. The Technical Modeler role will also ensure that the current systems engineering, and MS&A processes covered in the SOW requirements are maintained. Understand and guide the federated modeling approach across the enterprise to mission partners within the organization. Adhere to the MBSE modeling methodology guidelines and principles established by the team. Ability to demonstrate complex modeling concepts and best practices to different stakeholders. Basic Compensation: $150,000 - $187,000 USD annually The offered rate will be based on the selected candidate’s knowledge, skills, abilities and/or experience and in consideration of internal parity.
Work Environment
Location: On-site Travel Requirements: Minimal Working Hours: Standard Required Qualifications: An active/current TS/SCI with Polygraph is required to be considered for this position. Bachelor’s Degree in a Science, Technology, Engineering, or Mathematics (STEM) discipline and 10+ years of experience. Deep understanding of systems engineering, and an expert in Digital Engineering (DE)/Model-Based Systems Engineering (MBSE). IC Customer expertise with ground and/or space-based engineering within the customer's systems engineering teams. Excellent understanding of data science concepts and