Modeling, Analysis & Simulation (MA&S) Engineer

ResponsibilitiesPeraton 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 ResponsibilitiesDefine and maintain the enterprise MA&S strategy and architecture, establishing how models, simulations, and analytical tools are developed, governed, and reused across the integrated programLead 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 activitiesDevelop 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 boundariesEstablish model governance standards — configuration management, version control, validation criteria, and model pedigree tracking — to ensure model trustworthiness across teams and subcontractorsApply 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 repositoriesDevelop 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 supportLeverage generative AI and large language models (LLMs) to accelerate model documentation, translate between modeling formalisms, and assist engineers in querying and navigating complex system modelsImplement machine learning-based predictive analytics to identify integration risks, performance bottlenecks, and failure modes from historical simulation data and system telemetryEvaluate 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 contextsDesign and operate simulation environments — constructive, virtual, and hardware-in-the-loop — that replicate integrated system behavior for performance analysis, stress testing, and scenario explorationConduct trade-off analyses, sensitivity studies, and Monte Carlo simulations to quantify risk, evaluate design alternatives, and support engineering decision-makingDevelop integration simulation frameworks that allow multi-vendor components to be tested in a virtual integration environment prior to physical integration, reducing risk and accelerating deliveryPerform performance modeling and capacity analysis for real-time, low-latency, and high-availability systems, ensuring that integrated solutions meet stringent operational requirementsSupport verification and validation (V&V) activities by providing model-based evidence, simulation results, and analytical artifacts that demonstrate system compliance with requirementsCollaborate with enterprise architects, software architects, cybersecurity teams, and data architects to ensure models and simulations are aligned with broader architectural governance and design decisionsConduct technical reviews of vendor and subcontractor modeling deliverables to ensure alignment with enterprise MBSE standards, interface specifications, and model quality requirementsDefine and manage model exchange standards and interfaces across the integrated program, enabling interoperability between modeling tools used by different teams and subcontractorsTranslate complex modeling results, simulation outcomes, and AI-driven insights into clear, actionable guidance for program leadership, government stakeholders, and non-technical audiencesMentor and guide systems engineers, simulation developers, and data scientists in MBSE practices, simulation techniques, and the responsible application of AI in engineering workflows#BNATC#LSI2QualificationsRequired QualificationsPublic Trust Clearance - Ability to Obtain and Maintain15+ years of experience in modeling and simulation, systems engineering, or MBSE within large-scale programsBachelor'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 solutionsDeep 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 analyticsStrong command of simulation methodologies — discrete event simulation, agent-based modeling, Monte Carlo analysis, hardware-in-the-loop, and constructive simulation environmentsHands-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 developmentSolid understanding of systems engineering lifecycle processes — requirements analysis, architecture design, integration, verification, and validation — and how models support each phaseWorking knowledge of federal frameworks (NIST, FedRAMP, RMF) as they relate to the security and accreditation of modeling and simulation environmentsExperience supporting federal or DoD programs with complex integration and system-of-systems challengesBackground in mission-critical or safety-critical systems where model accuracy and simulation fidelity directly impact operational and safety outcomesProven ability to lead cross-functional teams and drive alignment across systems engineering, software development, test, and operations disciplinesPreferred QualificationsExperience with modeling and simulation for aviation, air traffic management, or national airspace systemsBackground in digital twin development — creating persistent, data-connected virtual representations of physical systems for ongoing monitoring and analysisFamiliarity with real-time simulation, low-latency modeling, and edge computing for simulation deploymentExperience 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 studiesKnowledge of data architecture and data integration as it relates to feeding operational data into models and simulations for calibration and validationITIL certification and experience aligning MA&S activities with ITSM and configuration management processesExperience with AIOps, observability platforms, and using operational telemetry to continuously refine and validate modelsFamiliarity with AI governance, model explainability, and trustworthy AI frameworks as applied to engineering decision supportRelevant 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 #LSIPeraton OverviewPeraton is a next-generation national security company that drives missions of consequence spanning the globe and extending to the farthest reaches of the galaxy. As the world’s leading mission capability integrator and transformative enterprise IT provider, we deliver trusted, highly differentiated solutions and technologies to protect our nation and allies. Peraton operates at the critical nexus between traditional and nontraditional threats across all domains: land, sea, space, air, and cyberspace. The company serves as a valued partner to essential government agencies and supports every branch of the U.S. armed forces. Each day, our employees do the can’t be done by solving the most daunting challenges facing our customers. Visit peraton.com to learn how we’re keeping people around the world safe and secure.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.EEOEEO: Equal opportunity employer, including disability and protected veterans, or other characteristics protected by law.Job SummaryRequisition ID: 2026-166387Position Category: EngineeringClearance: Public Trust