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
