AI/ML Engineer - Associate Staff

MIT Lincoln Laboratory•Lexington, MA

2d•$116,400 - $182,200

About The Position

The Advanced Concepts & Technologies Group (Group 39) is looking for exceptional engineers, scientists, and mathematicians who are excited to solve challenging problems. To improve operational effectiveness in rapidly evolving, complex threat environments, Group 39 conducts cutting-edge research and development in systems and architecture analysis; modeling and simulation; software and hardware prototyping and fielding; artificial intelligence; and signal processing. The group’s multidisciplinary team includes scientists and engineers with backgrounds in physics, mathematics, computer science, and engineering. Group 39 values inclusiveness, fosters mentoring at all levels, and promotes critical and innovative thinking to address the needs of the nation. The successful candidate will join a highly collaborative team that supports professional growth and is dedicated to solving important national security challenges. The Advanced Concepts and Technologies Group is seeking an Associate Staff candidate to develop and apply artificial intelligence, modeling, and data-analysis methods for technology development and assessment. Our team works with government sponsors to understand complex operational and technical problems, develop new capabilities, and quantitatively evaluate emerging technologies and concepts. The successful candidate will contribute across problem formulation, algorithm and software development, modeling, experimentation, and analysis. Working with government sponsors, mission subject-matter experts, scientists, and engineers, the candidate will translate open-ended questions into technical approaches and quantitative assessments. Work may involve deep learning, large language models, logic models, reinforcement learning, applied statistics, physics-based modeling, and analysis of complex structured and unstructured datasets. Depending on the problem, these methods may be applied through simulation, interactive environments, experimental prototypes, or other analysis tools.

Requirements

Master’s degree in Computer Science, Data Science, Statistics, Mathematics, Applied Mathematics, Physics, Engineering, Operations Research, or a related technical field. In lieu of a master’s degree, a bachelor’s degree with at least 2–3 years of directly relevant technical experience will be considered.
Demonstrated programming ability in Julia, Python, or a similar scientific programming language.
Experience in artificial intelligence, machine learning, or computational decision modeling, including practical experience in at least one of the following areas: deep learning, large language models, logic models, or reinforcement learning.
Demonstrated ability to formulate and solve complex, open-ended technical problems and independently develop software, models, algorithms, or analytical capabilities.
Working knowledge of applied statistics, experimental design, and quantitative methods, including experience selecting appropriate methods and interpreting results.
Experience analyzing complex, poorly structured, or unstructured datasets and extracting meaningful technical or operational insights.
Strong written and oral technical communication skills, including the ability to communicate technical concepts, assumptions, methods, and results clearly.
Ability to work independently, take ownership of technical tasks, and collaborate effectively within multidisciplinary teams.
Ability to rapidly develop expertise in new technical and mission areas.

Nice To Haves

Software engineering experience, including software architecture, modular and extensible design, automated testing, version control, code review, and technical documentation.
Experience applying AI/ML, modeling, or data analysis to technology development, system assessment, or decision support.
Experience with formal or logic-based modeling approaches, such as PDDL, signal temporal logic, automated planning, rule-based systems, or related methods.
Experience developing visualizations, user interfaces, or interactive tools for complex technical systems, including simulations, analytical applications, or serious games.
Familiarity with Department of War missions, operations, systems, or technologies.
Background in physics-based modeling, particularly radar systems, sensor performance, detection, tracking, or radio-frequency propagation.
Experience with Monte Carlo simulation, experimental design, uncertainty quantification, and/or sensitivity analysis.
Experience with agent-based models, decision-support tools, human-machine teaming, or autonomous decision-making systems.
Experience with high-performance, parallel, distributed, or GPU computing.
Experience leading focused technical tasks or working directly with government sponsors.
Active Secret clearance.

Responsibilities

Independently develop and apply AI, modeling, or analysis capabilities for technology development and assessment.
Work with government sponsors and subject-matter experts to translate operational and technical questions into quantitative analyses, technical requirements, and evaluation criteria.
Develop and evaluate AI/ML methods using deep learning, large language models, logic models, reinforcement learning, or related techniques.
Develop and integrate statistical, physics-based, and data-driven models of complex systems and operations.
Design and conduct quantitative studies and experiments, analyze complex structured and unstructured datasets, quantify uncertainty, and interpret technical and operational results.
Develop software prototypes and analysis tools and contribute to software architecture, testing, verification, validation, documentation, version control, and code review.
Take ownership of focused technical tasks and collaborate with multidisciplinary teams to identify appropriate technical approaches and next steps.
Communicate technical methods, assumptions, limitations, results, and recommendations through clear briefings, demonstrations, and written products for sponsor and technical audiences.