AL/ML Programmer

MIT Lincoln Laboratory•Lexington, MA

2d•$145,200 - $220,000

About The Position

The Artificial Intelligence Technology and Systems Group at MIT Lincoln Laboratory is seeking motivated applicants to 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, and 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 Masters and 5 + years of relevant experience.
Knowledge of artificial intelligence, ideally with applications to 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, differentiable modeling and simulation packages, 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.
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.
Must have, or the ability to obtain a full scope Top Secret Clearance.

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.