AI / Machine Learning Engineer with Security Clearance
Knowledge of artificial intelligence ideally with applications on multimedia, cyber security or adversarial machine learning / AI security experience.
Applied AI/ML knowledge – solid graduate-level coursework or equivalent work experience applying machine-learning theory, deep learning, NLP, computer-vision, graph analytics, or adversarial/AI-assurance techniques.
Proficient Python programming – comfortable writing clean, modular, and testable code; expert-level use of deep-learning frameworks (PyTorch, TensorFlow, etc.), data-science stacks (NumPy, pandas, SciPy, etc.), and hands-on experience with agentic/LLM-oriented toolkits (e.g., MCP).
Operating in a rapid research-to-prototype pipeline – demonstrated ability to take a cutting-edge research concept, design an experimental plan, implement a functional prototype, and iterate based on quantitative evaluation. Experience with benchmark datasets and realistic metrics (e.g. latency, accuracy, robustness) is a plus.
Software-engineering best practices – strong Git workflow (branching, pull-requests, code reviews), continuous-integration testing, environment reproducibility (e.g., conda, virtualenv). Ability to document code, write reproducible experiment notebooks, and maintain versioned releases. Desired Skills: Ability to read, evaluate, and implement state-of-the-art AI research papers.
Problem-solving & analytical mindset – can decompose novel, ill-defined problems, propose multiple solution paths, and select the most promising approach based on empirical evidence.Detail-oriented, able to multi-task, with the ability to work autonomously with minimal supervision.
Effective written and oral communication skills in technical environments, technical reports, internal wiki pages; delivers clear demo presentations and briefings to both technical audience and senior mission stakeholders.