Johns Hopkins Applied Physics Laboratory · 18 hours ago
Modeling and Simulation Engineer – Integration and Analysis
Johns Hopkins Applied Physics Laboratory (APL) is seeking a Modeling and Simulation Engineer to develop and deploy integrated modeling and simulation software applications. The role involves collaborating in cross-functional teams to optimize simulation workflows, analyze performance outputs, and deliver insights to stakeholders.
EducationUniversities
Responsibilities
You will work in cross-functional research teams to identify and recommend the most suitable modeling and simulation approaches for study objectives
You will architect integrated software solutions to optimize simulation workflows and support rigorous analysis
You will develop integration layers to connect software applications and enable them to interoperate
You will create and maintain scripts to orchestrate and automate large-scale simulation runs
You will insert algorithm prototypes into existing models, to support concept exploration and feasibility studies for the development of future advanced capabilities
You will diagnose and debug software defects while tracing and root causing anomalous simulation outcomes
You will analyze simulation outputs and translate results into clear insights for the research sponsor
You will deliver presentations to technical staff, program leadership, and government sponsors
Qualification
Required
Possess a Bachelor's degree in Math, Computer Science, Electrical Engineering or a related field
Have 2+ years of experience in software development and integration
Are proficient in Python and C++ with the ability to translate mathematical concepts into well-documented and efficient code
Are adept with software revision control practices (e.g.: Git)
Are familiar with software build systems like Make/CMake
Are comfortable using a debugger to troubleshoot code in a Linux environment
Can effectively communicate ideas and results
Are able to obtain an Interim Secret level security clearance by your start date and can ultimately obtain Secret level clearance. If selected, you will be subject to a government security clearance investigation and must meet the requirements for access to classified information. Eligibility requirements include U.S. citizenship
Preferred
Have an MS or higher degree in Engineering, Math, Computer Science, or a related field
Have 5+ years of experience in software development, integration, deployment, and maintenance
Are competent in a wide variety of programming languages, including MATLAB, C++, Python, and Java, on both Linux and Windows platforms
Are familiar with advanced programming language concepts such as object-oriented features, templates, lambdas, map-reduce techniques, and stream-style collection manipulation
Are proficient with Linux shell scripting (bash, zsh, etc.)
Have software development experience in parallel processing and multi-threaded applications
Have worked with discrete-time, event-driven simulations
Have experience with distributed simulation technologies like DIS, AFSIM, and HLA
Have experience using high-performance computing environments like GPUs and CPU clusters
Have knowledge and experience with AI/ML technologies and MLOps practices
Have work experience with containerization and running containers using Podman or Docker
Have the ability to obtain a Top Secret clearance
Benefits
Robust education assistance program
Unparalleled retirement contributions
Healthy work/life balance
Comprehensive benefits package including retirement plans, paid time off, medical, dental, vision, life insurance, short-term disability, long-term disability, flexible spending accounts, education assistance, and training and development
Company
Johns Hopkins Applied Physics Laboratory
The Johns Hopkins Applied Physics Laboratory (APL) is a not-for-profit university-affiliated research center (UARC) that provides solutions to complex national security and scientific challenges with technical expertise and prototyping, research and development, and analysis.
Funding
Current Stage
Late StageTotal Funding
unknownKey Investors
U.S. Department of Homeland Security
2023-01-17Grant
Leadership Team
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