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# AI / Machine Learning Modeler / Engineer - National Capital Regi

- **Company:** Synertex LLC
- **Location:** Chantilly, VA, United States
- **Experience:** Experienced
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Machine Learning, Reinforcement Learning, Deep Learning, Model Validation
- **Published:** August 14, 2026
- **Apply:** https://www.clearancejobs.com/jobs/9089063/ai-machine-learning-modeler-engineer-national-capital-regi

## About the Role

TS/SCI clearance with polygraph required, including additional security screenings

Education and Experience Requirements

Candidates must meet the following minimum education and relevant experience combinations by level:

Entry Level:

 * HS/GED + 6 years experience
 * Associate's + 4 years experience
 * Bachelor's + 2 years experience
 * Master's degree (experience may be substituted as appropriate)

Journeyman Level:

 * HS/GED + 8 years experience
 * Associate's + 6 years experience
 * Bachelor's + 4 years experience
 * Master's + 2 years experience

Senior Level:

 * HS/GED + 10 years experience
 * Associate's + 8 years experience
 * Bachelor's + 6 years experience
 * Master's + 4 years experience
 * PhD + 2 years experience

Expert / Master Level:

 * HS/GED + 12 years experience
 * Associate's + 10 years experience
 * Bachelor's + 8 years experience
 * Master's + 6 years experience
 * PhD + 4 years experience

Join a mission-driven team advancing government communication capabilities and operational readiness. Apply today and become part of Synertex LLC's legacy of innovation, leadership, and excellence.

## Description

Develops, deploys, and maintains AI and machine learning models in support of critical government and intelligence missions. This role applies advanced machine learning techniques to build, evaluate, and sustain models that operate in production environments with high performance, reliability, and scalability requirements., * Develop, deploy, and maintain AI and machine learning models using provided datasets and customer requirements.

 * Test and develop machine learning models to meet or exceed defined performance evaluation metrics.
 * Apply concepts of supervised, unsupervised, and reinforcement learning in model development.
 * Perform hyperparameter tuning and model evaluation to optimize performance.
 * Monitor machine learning models in production environments to ensure reliability, efficiency, and scalability.


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