
Machine Learning Engineer Senior (TS/SCI)
- Herndon, VA
- $131,000-219,000 per year
- Permanent
- Full-time
- Design, develop, and implement machine learning models and algorithms for a variety of applications.
- Build and maintain scalable and robust machine learning pipelines.
- Collect, clean, preprocess, and analyze large datasets.
- Evaluate model performance and identify areas for improvement.
- Collaborate with cross-functional teams, including software engineers, data scientists, and product managers, to integrate machine learning solutions into existing systems.
- Stay up-to-date with the latest advancements in machine learning and artificial intelligence.
- Contribute to the development of best practices and standards for machine learning development and deployment.
- Document machine learning models, processes, and results clearly and effectively.
- Troubleshoot and debug machine learning models and systems.
- Active TS SCI clearance with CI Poly
- Bachelor's degree in Computer Science, Data Science, Statistics, or a related field.
- 10 years of relevant experience; a relevant M.S. degree can be substituted for 2 years of experience
- Demonstrated experience with the application of machine learning and artificial intelligence.
- Strong programming skills in languages such as Python.
- Solid understanding of machine learning concepts, algorithms, and libraries (e.g., scikit-learn, TensorFlow, PyTorch).
- Experience with data manipulation and analysis using tools like Pandas and NumPy.
- Familiarity with cloud computing platforms (e.g., AWS, Azure, GCP).
- Demonstrated experience with the application of machine learning and artificial intelligence within the Department of Defense and/or the Intelligence Community.
- Excellent oral and written communication skills, with the ability to explain complex technical concepts to both technical and non-technical audiences.
- Experience with natural language processing (NLP), computer vision, or other specialized areas of machine learning.
- Experience with deploying machine learning models in production environments.
- Familiarity with containerization technologies (e.g., Docker, Kubernetes).
- Advanced degree (Master's or Ph.D.) in a relevant field.