Peraton is seeking an experienced Modeling, Analysis & Simulation (MA&S) Engineer to lead the development and application of models, simulations, and analytical frameworks that support the engineering, integration, and validation of complex BNATCS systems. In Peraton's role as a systems integrator, this position is essential to understanding how independently developed components will behave when brought together, long before physical integration occurs.

This role sits at the intersection of model-based systems engineering (MBSE), simulation science, and artificial intelligence — applying AI-augmented modeling techniques to accelerate system design, predict emergent behaviors, automate trade-off analyses, and enhance the fidelity and efficiency of simulation environments. You will define the MA&S strategy, build and govern the modeling ecosystem, and guide engineering teams in using models as the authoritative source of truth for design decisions, integration verification, and performance analysis across the enterprise.

This role is based in Herndon, VA.

Key Responsibilities

• Define and maintain the enterprise MA&S strategy and architecture, establishing how models, simulations, and analytical tools are developed, governed, and reused across the integrated program
• Lead model-based systems engineering (MBSE) initiatives using industry-standard tools and languages (Cameo, Sparx EA, MATLAB/Simulink, SysML, UAF) to create authoritative system models that drive requirements, design, integration, and verification activities
• Develop and maintain system-of-systems models that represent the integrated behavior of multi-vendor components, capturing interfaces, dependencies, data flows, and emergent properties across technical and organizational boundaries
• Establish model governance standards — configuration management, version control, validation criteria, and model pedigree tracking — to ensure model trustworthiness across teams and subcontractors
• Apply AI and machine learning techniques to enhance MBSE workflows — including automated model generation from requirements, natural language processing (NLP) for requirements analysis, and AI-assisted consistency and completeness checking across large model repositories
• Develop and deploy AI-driven surrogate models and digital twins that approximate high-fidelity simulations at reduced computational cost, enabling rapid design space exploration and real-time decision support
• Leverage generative AI and large language models (LLMs) to accelerate model documentation, translate between modeling formalisms, and assist engineers in querying and navigating complex system models
• Implement machine learning-based predictive analytics to identify integration risks, performance bottlenecks, and failure modes from historical simulation data and system telemetry
• Evaluate and integrate emerging AI-for-engineering tools into the MA&S toolchain, assessing their maturity, trustworthiness, and applicability to safety-critical and mission-critical modeling contexts
• Design and operate simulation environments — constructive, virtual, and hardware-in-the-loop — that replicate integrated system behavior for performance analysis, stress testing, and scenario exploration
• Conduct trade-off analyses, sensitivity studies, and Monte Carlo simulations to quantify risk, evaluate design alternatives, and support engineering decision-making
• Develop integration simulation frameworks that allow multi-vendor components to be tested in a virtual integration environment prior to physical integration, reducing risk and accelerating delivery
• Perform performance modeling and capacity analysis for real-time, low-latency, and high-availability systems, ensuring that integrated solutions meet stringent operational requirements
• Support verification and validation (V&V) activities by providing model-based evidence, simulation results, and analytical artifacts that demonstrate system compliance with requirements
• Collaborate with enterprise architects, software architects, cybersecurity teams, and data architects to ensure models and simulations are aligned with broader architectural governance and design decisions
• Conduct technical reviews of vendor and subcontractor modeling deliverables to ensure alignment with enterprise MBSE standards, interface specifications, and model quality requirements
• Define and manage model exchange standards and interfaces across the integrated program, enabling interoperability between modeling tools used by different teams and subcontractors
• Translate complex modeling results, simulation outcomes, and AI-driven insights into clear, actionable guidance for program leadership, government stakeholders, and non-technical audiences
• Mentor and guide systems engineers, simulation developers, and data scientists in MBSE practices, simulation techniques, and the responsible application of AI in engineering workflows

#BNATC

#LSI2

Required Qualifications

• Public Trust Clearance - Ability to Obtain and Maintain
• 15+ years of experience in modeling and simulation, systems engineering, or MBSE within large-scale programs
• Bachelor's degree in Systems Engineering, Computer Science, Aerospace Engineering, Mathematics, Physics, or a related field (or 4 additional years of relevant experience in lieu of degree)
• Demonstrated experience serving as an MA&S lead within a systems integrator environment, developing models and simulations that validate multi-vendor, multi-technology integrated solutions
• Deep expertise in model-based systems engineering (MBSE) using SysML, UAF, or equivalent modeling languages and tools (Cameo, Sparx EA, MATLAB/Simulink, Rhapsody)
• Proven experience applying AI and machine learning techniques to engineering modeling workflows — surrogate modeling, automated analysis, NLP-based requirements processing, or predictive analytics
• Strong command of simulation methodologies — discrete event simulation, agent-based modeling, Monte Carlo analysis, hardware-in-the-loop, and constructive simulation environments
• Hands-on experience with simulation frameworks and tools (AnyLogic, Arena, AFSIM, STK, custom simulation engines, or equivalent)
• Proficiency in Python, MATLAB, Java, or other languages commonly used in modeling, simulation, and AI/ML development
• Solid understanding of systems engineering lifecycle processes — requirements analysis, architecture design, integration, verification, and validation — and how models support each phase
• Working knowledge of federal frameworks (NIST, FedRAMP, RMF) as they relate to the security and accreditation of modeling and simulation environments
• Experience supporting federal or DoD programs with complex integration and system-of-systems challenges
• Background in mission-critical or safety-critical systems where model accuracy and simulation fidelity directly impact operational and safety outcomes
• Proven ability to lead cross-functional teams and drive alignment across systems engineering, software development, test, and operations disciplines

Preferred Qualifications

• Experience with modeling and simulation for aviation, air traffic management, or national airspace systems
• Background in digital twin development — creating persistent, data-connected virtual representations of physical systems for ongoing monitoring and analysis
• Familiarity with real-time simulation, low-latency modeling, and edge computing for simulation deployment
• Experience with cloud-based simulation environments (AWS, Azure) including high-performance computing (HPC), containerized simulation workloads, and scalable compute for large-scale Monte Carlo or parametric studies
• Knowledge of data architecture and data integration as it relates to feeding operational data into models and simulations for calibration and validation
• ITIL certification and experience aligning MA&S activities with ITSM and configuration management processes
• Experience with AIOps, observability platforms, and using operational telemetry to continuously refine and validate models
• Familiarity with AI governance, model explainability, and trustworthy AI frameworks as applied to engineering decision support
• Relevant certifications such as INCOSE CSEP/ESEP, AWS Solutions Architect, or certifications in AI/ML (e.g., AWS Machine Learning Specialty, Google Professional ML Engineer)

#BNATCS #LSI

Target Salary Range: $135,000 - $216,000. This represents the typical salary range for this position. Salary is determined by various factors, including but not limited to, the scope and responsibilities of the position, the individual’s experience, education, knowledge, skills, and competencies, as well as geographic location and business and contract considerations. Depending on the position, employees may be eligible for overtime, shift differential, and a discretionary bonus in addition to base pay.

Benefits Statement: Peraton offers eligible employees a variety of benefits including medical, dental, vision, life, health savings account, short/long term disability, EAP, parental leave, 401(k), paid time off (PTO) for vacation, and company paid holidays. A full listing of available benefits can be viewed at https://www.careers.peraton.com/benefits.

Application Statements: The application period for the job is estimated to be 30 days from the job posting date. However, this timeline may be shortened or extended depending on business needs and the availability of qualified candidates. By applying to this job, you are expressing interest in the role and the Company. During the review of your application, you may be required to participate in an on-camera interview, as well as participate in a process to verify your identity.