SAIC is providing key design, installation, and research support to facilitate a successful implementation of a multilevel secure (MLS) Live Virtual Constructive (LVC) simulation environment meant to allow a cohesive a training and experimentation environment between models and sims typically separated by physical network segmentation. This project is on the forefront of new technologies and concepts, and SAIC will design, build, and test systems meant to challenge the existing paradigm of the USAF enterprise network architecture. As such, the ideal candidate will have significant experience in both modeling and simulation (M&S) and network engineering. Please note, this position can be done anywhere in the United States, but you will work an East Coast schedule as a remote employee at Hanscom AFB.
The Simulation Engineer performs research and experimentation to test MLS and Zero Trust (ZT) technologies, tools, and architectures to support LVC simulation environments with highly variable requirements for fidelity, resolution, throughput, and latency. This position will be part of a highly technical team in order to research, experiment, and report on commercially available MLS and ZT alternatives.
- Explore, analyze and report on applications, alternative MLS solutions, or other areas for improvement to the customer’s LVC M&S baseline.
- Demonstrate suitability of the customer’s IT environment by exploring specific improvements.
- Capture best practices to develop modeling and simulation (M&S) software including development of MLS capable/aware applications.
- Compare current system capabilities to proposed ZT enablers and create a technical suitability matrix to highlight the potential benefits of adoption of new methodologies, architectural changes, and software security tools to enable ZT architectures.
- Examine existing ZT tools/technologies/architectures and recommend enhancements.
- Assess ZT implementation in an objective network, and assess ZT components.
- Using the DoD Cybersecurity Architecture Review (DoDCAR) process, NIST SP 800-207, MITRE ATT&CK framework, and industry best practices, analyze and identify the thread gaps and the resulting changes to explore enhancements to the customer’s MLS and ZTA solution.
- Perform gap and trade-off analyses to weigh the potential benefits of integrating the proposed changes and outline relative risk and benefit to community of interest operations.
- Develop a test schedule and test criteria for viable MLS and ZT alternatives within a exemplar LVC simulation environment.
- Execute testing on MLS and ZT alternatives IAW the developed test plan and criteria.
- Analyzes system designs for risks to data confidentiality, integrity, and availability.
- Bachelors and eighteen (18) years or more experience; Masters and sixteen (16) years or more experience; PhD or JD and fifteen years or more experience
- At least 3 years of modeling and simulation (M&S) experience, either as a user or as a developer
- At least 3 years of network engineering experience
- At least 2 years of experience with virtualization platforms
- Security + CE or CCNA-Security certification is required.
- Solid understanding of ZT tenets as detailed in NIST SP 800-207.
- Experience in developing test plans and criteria for commercial products.
- Able to rapidly research and experiment on relevant commercial solutions in a lab environment.
- Strong interpersonal and communication skills (verbal and written).
- Responsive and reliable.
Preferred Experience, Education, and Certifications:
- Experience and familiarity in available commercial solutions enabling MLS and/or incorporating ZT tenets.
- Experience and familiarity with obtaining Authorities to Operate (ATO) and Interim ATO’s for simulation experiments and exercises.
- Familiarity with DoDCAR process and MITRE ATT&CK framework.
- Certified Modeling and Simulation Professional (CMSP) certification is a plus.
Target salary range: $195,001 - $205,000. The estimate displayed represents the typical salary range for this position based on experience and other factors.