Modeling & Simulation Engineer for DoD Systems

Johns-Hopkins-University-Applied-Physics-Laboratory; Laurel (MD); On-site; USD 100,000 - 245,000; Full time

Job summary

The Johns Hopkins University Applied Physics Laboratory (APL) is seeking a Modeling & Simulation Engineer to develop and integrate software models of combat system elements for AWS and other defense systems. You will join a collaborative team that validates models to support flight tests and provide confidence in capabilities.

Responsibilities include prototyping algorithms, integrating with simulation tools, verifying high-fidelity models, and identifying suitable modeling approaches while

Qualifications

• BS in Computer Science, Electrical Engineering, Computer Engineering, Physics, Mathematics, or related field.
• Proficient in modern C++ and Python.
• Proficient in using software revision control tools in a DevOps environment.
• 2+ years of experience as a software engineer or in developing modeling and simulation tools.
• U.S. citizenship and the ability to obtain Interim Secret and eventually Final Secret clearance.

Responsibilities

• Develop and prototype algorithms for new capabilities and integrate with simulation tools.
• Assist in verification and validation of high-fidelity combat system models.
• Collaborate with multi-functional teams to review system requirements and identify deficiencies.
• Architect integrated software solutions to optimize simulation workflows and analysis.

Skills

C++

Python

DevOps

Education

BS in Computer Science or related field

MS in related field (preferred)

Tools

Version control (Git)

Job description

The Johns Hopkins University Applied Physics Laboratory (APL) is seeking a Modeling & Simulation Engineer to develop and integrate software models of combat system elements for AWS and other defense systems. You will join a collaborative team that validates models to support flight tests and provide confidence in capabilities.

Responsibilities include prototyping algorithms, integrating with simulation tools, verifying high-fidelity models, and identifying suitable modeling approaches while