| Location | Springfield, Virginia |
Develop and refine advanced machine learning solutions to identify, classify, and analyze patterns and activities across complex sensor datasets.
Lead the full ML development lifecycle, including data preparation, model training, validation, performance tuning, and deployment into mission-focused environments.
Collaborate with technical researchers, scientists, and mission specialists to understand unique data challenges and develop effective AI/ML solutions.
Build scalable, reliable Python applications following software engineering best practices, with an emphasis on maintainability, testing, and documentation.
Assess model accuracy, reliability, and overall effectiveness, identifying opportunities to improve performance across varying datasets and operational conditions.
Incorporate machine learning models into established software systems and analytical tools, ensuring successful integration and operational readiness.
Provide technical guidance to less experienced engineers through mentorship, code reviews, and the development of consistent engineering practices.
Support technical evaluations, solution design, and technology selection by assessing different machine learning methodologies and architectures.
Communicate technical findings, project progress, and recommendations through written reports, presentations, and discussions with program leadership and government stakeholders.
Active Top Secret clearance with eligibility to obtain SCI access and a CI Polygraph.
Bachelor's degree in Computer Science, Engineering, Physics, Mathematics, Data Science, or a related STEM discipline. Master's degree or PhD preferred.
At least 7 years of professional machine learning experience, including hands-on development and implementation of AI/ML solutions in production or operational settings.
Strong understanding of deep learning techniques, including convolutional neural networks (CNNs), transformer architectures, computer vision, and object detection methodologies.
Advanced Python development skills and practical experience using frameworks such as PyTorch, TensorFlow, or comparable ML technologies.
Experience designing and managing end-to-end machine learning workflows, including data processing, labeling, model development, testing, and deployment.
Ability to partner with researchers and technical subject matter experts to convert complex scientific or operational requirements into practical machine learning applications.
Previous experience providing technical leadership, mentoring engineers, reviewing code, and promoting software development best practices.
Excellent verbal and written communication skills, with the ability to explain complex technical concepts and present findings to both technical and nontechnical audiences.