AI/ML Research Scientist

MIT Lincoln Laboratory•Ft Meade, MD

6d•$145,200 - $220,000•Onsite

About The Position

The Artificial Intelligence Technology and Systems Group at MIT Lincoln Laboratory has over 50 years of experience developing revolutionary technologies for critical national missions. This position is based at our field site in Fort Meade, MD. Candidates must be willing and able to commute to and work on-site and will regularly interface directly with government sponsors in secure facilities. Group 52 AI Technology & Systems specializes in machine learning (ML) algorithms, technologies, and systems that extract and analyze information from multimedia data—including speech, text, images, and video. In recent years, we have significantly expanded our mission to include developing impactful ML solutions for cybersecurity in collaboration with the Nation’s top cyber organizations. As a recognized leader in both basic and applied AI/ML, our group is shaping emerging AI fields and driving the Laboratory’s efforts in AI Assurance across the Department of Defense and the Intelligence Community. We also integrate expertise in multimedia, cyber, and AI assurance to develop cutting-edge technologies for Operations in the Information Environment (OIE). A hallmark of our work is a focus on operational relevance: we design and evaluate AI/ML systems using realistic datasets and metrics, and we partner directly with intelligence analysts and cyber operators to ensure rapid transition of our technologies into real-world, mission-critical systems. The AI Technology and Systems Group is seeking motivated applicants who can contribute to projects addressing a wide range of national needs in AI and ML. Assignments may include research on state-of-the-art algorithms in signal processing, natural language processing, graph analytics, or adversarial AI; applying innovative methods to challenging, real-world problems; and working in a collaborative, team-oriented development environment. This is an opportunity to apply your skills to some of the most important technical challenges of our time—while learning from and contributing to a world-class research community.

Requirements

Ph.D in electrical engineering, computer science, or another relevant discipline or a MS with 5 or more years of relevant experience or a BS and 9 or more years of relevant expereince will be considered.
Current TS/SCI clearance required (must be active at the time of application).

Nice To Haves

Knowledge of artificial intelligence ideally with applications on multimedia, cyber security or adversarial machine learning / AI security experience.
Deep AI/ML experience – graduate-level (or professional) knowledge of 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, define quantitative success metrics and measures, and produce a task-breakdown structure that aligns with program/project milestones. Adept at formulating hypothesis, designing controlled experiments, and drawing data-driven conclusions on novel problems.
Operate within a rapid research-to-prototype pipeline – demonstrable ability to turn novel algorithms and approaches into robust, reproduceable prototypes in tight timescales, 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.).
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.
Team and 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.

Responsibilities

Design, program, and architect advanced ML methods.
Develop algorithms in speech, natural language processing, multimedia, cyber, and graph analytics.
Publish and present research at premier conferences.