AI​/ML Engineer

The Artificial Intelligence/Machine Learning (AI/ML) Engineer develops AI/ML algorithms, cloud computing, and/or heterogeneous distributed computing infrastructures to support the deployment of AI/ML applications. The AI/ML Engineer also researches the mathematical foundations and frameworks for nonlinear systems characterized by time-varying and emerging dynamics of evolving or adaptive systems. The AI/ML Engineer develops technical solutions at the leading edge of Artificial Intelligence, Machine Learning, Genetic Programming, Computer Vision, and advanced data processing, filtering, and fusion techniques in high-performance computing and distributed heterogeneous computing environments.

The AI/ML Engineer writes parallel processing programs to deploy ML models developed by data scientists into more complex systems. The AI/ML Engineer has familiarity with state-of-the-art, open-source software frameworks and high-performance computing accelerators for machine learning. When conducting research, the AI/ML Engineer leverages the most recent advances in statistical analysis of large data sets to advance state-of-the-art automated sensor and data processing for a broad range of intelligent and sensor-enabled systems.

• Design complex system architectures (e.g., high-performance computing clusters, networks, chipsets, GPUs) based on available hardware (e.g., embedded systems, cloud, on-premise, etc.)
• Lead a team of engineers responsible for system deployment
• Develop novel algorithms and methodologies
• Engage with sponsors to understand and meet system requirements
• Serve as the primary author on technical reports and proposals

• Develop, implement, and apply machine learning algorithms and methods to support sponsor and internal research and development projects
• Contribute to research reports, white papers, and competitive proposals

• Experience with machine learning tools such as Tensorflow, PyTorch, and Scikit-learn
• Experience in the applied Artificial Intelligence and Machine Learning (AI/ML) domain particularly in the area of Machine Learning Operations (MLOps)
• Familiarity with software development and collaboration tools such as Git Lab and Atlassian (Confluence, Jira, Bitbucket)
• Knowledge and experience working with cloud computing platforms (e.g., Azure, AWS, GCP)

• Research and evaluate AI models, conduct validations, manage AI projects, and align with industry data quality and machine learning best-practices
• Provide feedback, input, and consultation on various artifacts, including technical analysis of vendor products, academic insights, inputs to the DoD Enterprise AI strategy and technical documentation
• Provide technical analysis on vendor products through market research and participation in vendor demos, offering insights into their capabilities
• Share expertise and keep teams informed about emerging trends on AI advancement through presentation, literature summaries, or curated resource list
• Research and evaluate cost-effective AI/ML solutions for DoD enterprise use cases.