Principal – Principal Systems MBD Engineer

Principal – Principal Systems MBD Engineer - Hybrid

Towson, MD, United States

This position requires onsite work three days per week at our Towson, MD campus.

The Systems Model Based Development Engineer will lead the development of advanced plant models, simulation frameworks, and engineering applications supporting next‑generation electromechanical product development.

• Lead development of high‑fidelity plant models for complex electromechanical systems using Dymola, Twin Builder, Amesim, Modelica, Simulink, and Simscape or equivalent tools.
• Develop multi‑scale and multi‑domain models spanning component, subsystem, and system‑level behavior across electrical, mechanical, thermal, hydraulic, pneumatic, and controls‑coupled domains.
• Translate abstract methods, first‑principles equations, reduced‑order formulations, and engineering concepts into practical, validated, and reusable simulation models.
• Perform custom model and library development to represent unique physics, behaviours, interfaces, and product architectures not available in standard toolsets.
• Build supporting engineering applications, scripts, utilities, and automation workflows to improve model usability, execution speed, and integration into development processes.
• Support code generation, model compilation, solver configuration, co‑simulation setup, FMU export/import, and runtime integration across development environments.
• Create interfaces between plant models and external applications, controllers, embedded software components, and test platforms.
• Partner with CAE engineers to align system‑level and reduced‑order models with detailed simulation results, test data, and product physics.
• Collaborate with MBD and controls engineers to provide plant models suitable for MIL, SIL, HIL, and closed‑loop controls development.
• Work with embedded software engineers to support executable deployment, runtime validation, and integration of models into software and test ecosystems.
• Perform model verification, validation, calibration, sensitivity analysis, and performance optimization using simulation and test data.
• Contribute to simulation architecture decisions related to model reuse, interoperability, deployment strategy, abstraction level, and framework scalability.
• Document model assumptions, governing equations, parameter sources, interfaces, limitations, and validation results.
• Provide technical leadership and mentoring in plant modeling, multi‑scale simulation methods, and custom model/application development.

• Master’s or Ph.D. in Electrical Engineering, Mechanical Engineering, Aerospace Engineering, Controls Engineering, Applied Physics, Mechatronics, or a related technical field.
• 10+ years of relevant experience in plant modeling, model‑based design, physical system simulation, or engineering application development.
• Strong hands‑on experience with one or more plant modeling environments such as Dymola, Twin Builder, Amesim, Modelica‑based tools, Simulink, or Simscape.
• Demonstrated expertise in plant modeling, multi‑scale modeling, and multi‑domain physics‑based simulation.
• Proven ability to translate abstract methods, mathematical formulations, and first‑principles physics into executable and validated engineering models.
• Experience with custom model development, user‑defined components, libraries, or frameworks for simulation environments.
• Strong foundation in physics, mathematics, numerical methods, and dynamic systems.
• Strong technical background in electrical systems and their interaction with mechanical and control domains in electromechanical products.
• Experience with application development, scripting, or software/tool development to support simulation workflows and automation.
• Experience interfacing models with external software or simulation environments, including compilation, runtime execution, co‑simulation, or executable deployment.
• Ability to collaborate effectively across CAE, MBD, embedded software, controls, hardware, and systems engineering teams.
• Strong written and verbal communication skills with the ability to explain model assumptions, limitations, and results clearly.

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