About Durandal At Durandal, quality isn't just a standard, it is part of who we are. We take pride in developing industry-leading technology across four core areas of expertise: Software Engineering, Modeling and Simulation, Electronic Warfare, and AI/ML Engineering. Our talented team of developers and engineers brings curiosity, expertise, and a shared commitment to excellence in everything we do. Together, we challenge ourselves to build software and technology that is powerful, accurate, intuitive, and dependable, solving complex problems and making a real difference for our customers. We celebrate the teamwork, innovation, and attention to detail that make it all possible. Join our team and become part of a talented, collaborative, and driven group of people who are passionate about what we do. At Durandal, you'll have the opportunity to make an impact, grow your skills, tackle meaningful challenges, and help shape our future technology. Basic Qualifications Bachelor's degree in engineering, computer science, physics, or mathematics from an ABET-accredited or equivalent program. 3-8 years of professional experience in constructive modeling and simulation, systems engineering, or a directly related discipline. U.S. citizenship required; AFSIM is export-controlled and distribution-limited, so applicant must be a U.S. person under ITAR. Active DoD Secret clearance, or eligibility to obtain one. Required Experience 3-8 years developing, extending, and validating AFSIM models — platforms, movers, sensors, weapons, comm devices, and processors — including custom C++ component and plugin development against the AFSIM core framework.

Demonstrated AFSIM scenario generation:

authoring force laydowns, engagement chains, ROE and behavior logic in AFSIM script; building parameterized, reusable scenario templates that support repeatable trade studies rather than one-off runs. Execution and analysis of Monte Carlo campaigns in AFSIM — batch and distributed run management, seed control, event/CSV output pipelines — with results traced back to defined MOEs and MOPs. Hands-on proficiency across the AFSIM toolchain: Wizard for scenario development, Warlock for interactive/operator-in-the-loop execution, and Mystic for post-run visualization and event reconstruction. MBSE model authorship and maintenance in Cameo Systems Modeler / MagicDraw (or IBM Rhapsody) using

SysML:

requirement, block definition, internal block, activity, sequence, and state machine diagrams. Establishing and maintaining bidirectional traceability from system requirements to architecture model elements to simulation cases, including impact analysis when requirements or interfaces change. Driving simulation configuration from the architecture model — treating the MBSE artifact as the authoritative source for scenario and model parameters instead of maintaining a separate, divergent set of sim inputs. Python for simulation data engineering and analysis — NumPy, pandas, scikit-learn, matplotlib — plus disciplined use of Git and CI in a collaborative development environment. Modern C++ (C++14/17) development on Linux with CMake-based builds. Preferred Qualifications/Experience Working knowledge of RF, EW, and sensor phenomenology as represented in constructive simulation — radar detection and tracking models, EA/EP effects, RCS, propagation and atmospheric models — sufficient to assess model fidelity against SME input and measured data.

Model verification and validation practice:

documenting assumptions, limitations, and calibration evidence in a form that supports VV&A per DoDI 5000.61. Distributed simulation interoperability — integrating AFSIM with external simulations, live assets, or hardware-in-the-loop via DIS and/or HLA. Written and verbal communication sufficient to brief analysis results and modeling assumptions to government customers and non-M&S stakeholders. Applied AI/ML in a simulation context; for example: reinforcement learning, behavior trees, or utility-based decision logic for autonomous agent behavior within AFSIM; surrogate/metamodel development (Gaussian process, neural network regression) to compress large trade spaces and reduce campaign runtime; clustering, classification, or anomaly detection applied to simulation output to identify driving parameters and outlier engagement outcomes. Active TS/SCI clearance. AFSIM training completed through AFRL or an authorized provider; active participation in the AFSIM user community. Experience with other constructive or engagement-level simulations: EADSIM, Suppressor, ODESSA, ITASE, BRAWLER, ESAMS, STK, NGTS, or Simulink-based engagement models. Cameo Teamwork Cloud / DataHub administration, model federation, or custom profile and validation-rule development. Mission engineering and kill chain / kill web analysis; developing mission threads that tie system-level performance to campaign-level outcomes. Digital engineering ecosystem work aligned to the DoD Digital Engineering Strategy — authoritative source of truth, model curation, and configuration control. Containerized simulation deployment (Docker/Podman) and HPC scheduling (Slurm) for large campaign execution. Prior work on threat representation, threat model accreditation, or intelligence-informed adversary characterization.

Pay:

$105,000.00 - $145,000.00 per year

Benefits:

401(k) Dental insurance Health insurance Life insurance Paid time off Relocation assistance Retirement plan Vision insurance

Work Location:

In person