Leveraging our mission-ready technology and solutions, proven expertise in federal deployment, and strategic relationships, we enhance outcomes for the government, efficiently and effectively. With a focus on agility and collaboration, LMI serves the defense, space, healthcare, and energy sectors—helping agencies navigate complexity and outpace change. Headquartered in Tysons, Virginia, LMI is committed to delivering impactful results that strengthen missions and drive lasting value.

Responsibilities Our team specializes in crafting Digital Engineering analytical pipelines with a Model Based Systems Engineering (MBSE) data model front end.  These pipelines employ a micro-services-based architecture on a cloud-deployed system (AWS Govcloud) to maximize nation-wide system availability, scalability, data integrity, and security.  They support US Space Force organizations with highly responsive cutting-edge analytical insights which are helping change how the space domain supports the joint war fight.

• Analyze the current M&S platform architecture and identify areas for AI/ML integration.
• Develop and implement machine learning models to support simulation enhancement (e.g., behavior modeling, anomaly detection, optimization).
• Collaborate with subject matter experts to design intelligent agents, scenario adaptation, or surrogate models.
• Deploy and validate models in simulated environments to ensure performance, stability, and scalability.
• Contribute to documentation, testing, and version control of all developed models.
• Mentor or advise teams on AI/ML integration best practices.

Required Qualifications

• Bachelor’s or Master’s degree in Computer Science, Data Science, Electrical Engineering, Applied Mathematics, or a related field.
• 8+ years of experience in machine learning and/or data science applications.
• Strong Python programming skills; experience with TensorFlow, PyTorch, or Scikit-learn.
• Familiarity with simulation environments or agent-based modeling frameworks.
• Demonstrated ability to integrate AI/ML models into software systems.
• Excellent communication and collaboration skills.
• Space Modeling and Simulation Data Science experience.

Desired

• Previous Space Experience
• Experience with existing M&S tools
• Experience with reinforcement learning or intelligent agent design.
• Experience supporting defense or aerospace customers.
• Active U.S. Secret clearance or eligibility.
• Exposure to cloud-based MLOps tools (AWS SageMaker, Azure ML, etc.).

Target salary range: $119,000 - $207,714

Disclaimer The salary range displayed represents the typical salary range for this position and is not a guarantee of compensation. Individual salaries are determined by various factors including, but not limited to location, internal equity, business considerations, client contract requirements, and candidate qualifications, such as education, experience, skills, and security clearances.