Technical Lead - Digital Twin Research and Aviation Modeling - Peraton

Responsibilities

Peraton Labs is seeking a Technical Lead to directly support digital twin research and development efforts within the BNATCS CTO organization by helping shape, coordinate, and execute technical efforts related to aviation digital twins, NAS modeling and simulation, data integration, and future-state airspace modernization analysis. Within this role you will help ensure digital twin efforts are not just technically sophisticated, but also usable, explainable, operationally relevant, and aligned to the long-term modernization of the National Airspace System.

The ideal candidate will bring a strong combination of aviation domain knowledge, modeling and simulation experience, systems engineering judgment, and technical leadership. This person will help translate FAA modernization needs into executable digital twin concepts, simulation requirements, analytical products, and technical roadmaps that support BNATCS planning, validation, and decision-making.

This role is intended for a senior technical leader who can work with aviation SMEs, systems engineers, data scientists, AI/ML engineers, software teams, program leadership, and FAA stakeholders to ensure digital twin efforts are technically credible, operationally relevant, and aligned to FAA BNATCS mission objectives.

Key responsibilities may include:

• Help lead technical planning and execution of digital twin research efforts supporting BNATCS CTO objectives
• Support the development of digital twin concepts, architectures, use cases, and technical roadmaps for NAS modernization
• Assist with adapting Peraton Labs’ agentic AI and digital twin capabilities to BNATCS program needs
• Define modeling and simulation requirements based on FAA operational needs, system engineering objectives, safety considerations, and stakeholder priorities
• Guide the design of digital twin capabilities representing NAS elements such as airspace operations, surveillance, communications, facilities, ground systems, procedures, and human-in-the-loop considerations
• Establish model interoperability approaches, data exchange expectations, model fidelity guidance, and integration patterns across digital twin and simulation environments
• Work with data, AI/ML, software, and systems engineering teams to identify and integrate required data sources, data dependencies, model inputs, outputs, assumptions, and validation methods
• Evaluate how digital twin environments can support operational analysis, system validation, technology insertion, facility modernization, outage analysis, resilience planning, and future NAS concept evaluation
• Identify technical risks, dependencies, integration challenges, sequencing concerns, and capability gaps associated with digital twin development
• Translate complex simulation results, model outputs, and analytical findings into clear, actionable insight