Job Description

** TL/DR : This top-caliber AI/ML Researcher role is based on-site with an elite cyber team in Annapolis Junction, MD and requires active TS/SCI with Full Scope Polygraph, ideally with current accounts with NSA and/or USCC .**

Rogue Talent is hiring a Senior Researcher & Strategist in Artificial Intelligence, Deep Machine Learning and Cybersecurity to contribute to world‑class R&D with real impact on national security. This role will lean heavily on AI/ML engineering expertise, with an understanding of and driving curiosity for cybersecurity, red teaming, etc. This key individual will lead efforts in developing impactful AI/ML solutions, algorithms and backend systems for cybersecurity in collaboration with the nation’s top deterrence-focused organizations.

For our longest running client on the east coast, this position is based at their field site in Fort Meade, MD. Candidates must have TS/SCI with FSP, and be willing & able to commute to and work on‑site. This technical leader-researcher will regularly interface directly with government sponsors in secure facilities.

A hallmark of this small but elite (and rapidly expanding) team's work is operational relevance: designing and evaluating AI/ML systems using realistic datasets and metrics, while partnering directly with intelligence analysts and cyber operators to ensure rapid transition of prototypes & technologies into real‑world, mission‑critical systems.

What You'll Do:

• Design, program, and architect advanced ML methods.
• Research & develop state-of-the-art algorithms in signal processing, speech / NLP, multimedia, cyber, and graph analytics.
• Publish and present research at premier security/government conferences.

This is an opportunity to apply your skills to some of the most important technical challenges of our time, while learning from one of the most prestigious peer networks and contributing to a world‑class research community.

What You'll Need:

Candidates must possess a Ph.D in electrical engineering or computer science, a relevant Masters with 5 years of experience, or a BS + 7 years of applied, practical experience. Current, active TS/SCI clearance with full-scope polygraph is required.

• Knowledge of artificial intelligence applied to multimedia, cybersecurity and/or adversarial machine learning / AI security.
• Deep AI/ML experience: state-of-the-art machine-learning, deep-learning, LLM / Agentic AI, multimodal perception, graph analytics, adversarial/AI-assurance, or cyber-ML; ability to read, critique, and extend advanced research papers.
• System analysis & problem decomposition: can translate high-level end-state requirements into a clear set of research tasks, metrics and measures.
• Operate within a rapid research-to-prototype pipeline: demonstrable ability to turn novel algorithms and approaches into robust, scalable prototypes in highly autonomous but tight timeframes, around iterative refinement.
• Agentic AI framework experience: hands-on work with MCP or comparable toolkits; able to design, implement, and evaluate end-to-end LLM-driven agents.
• Advanced Python & ML libraries: expert-level proficiency in Python and deep-learning frameworks (PyTorch, TensorFlow, etc.), plus Huggingface ecosystem and data science frameworks (NumPy/pandas/SciPy, etc.).
• SWE best practices: strong Git workflow (branching, pull-requests, code reviews), continuous-integration testing, environment reproducibility (e.g., conda, virtualenv). Ability to document code, write experiment notebooks, and maintain versioned releases.
• Team & project leadership: experience leading small research teams, assigning tasks, tracking progress, removing technical blockers, and mentoring junior staff.
• Stakeholder communication: strong written and oral skills for prepping technical briefings, composing white-papers, demo presentations, and funding proposals; able to convey complex research outcomes to sponsors, senior leadership, and external partners.
• Detail-oriented, able to multi-task, with the ability to work autonomously, set technical direction, and operate with minimal supervision.

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