Modeling, Analysis & Simulation (MA&S) Engineer

Peraton•Herndon, VA

19h•Onsite

About The Position

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.

Requirements

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

Nice To Haves

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)

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