Applied AI/ML Researcher

MIT Lincoln Laboratory•Lexington, MA

1d•$114,000 - $164,000

About The Position

Group 51 at MIT Lincoln Laboratory develops advanced Cyber-Electromagnetic Warfare capabilities and supports their acquisition, integration, and operational transition across the Department of War and Intelligence Community. The Group conducts research and development at the intersection of cyber operations, RF systems, and artificial intelligence, applying electromagnetic theory, signal processing, statistical inference, experimental methods, and modern AI to national-security mission needs. Its work includes identifying and characterizing signals, protocols, and emitters, developing prototype capabilities, and integrating and testing technologies in laboratory, field, and operationally relevant settings. The Applied AI/ML Researcher will join multidisciplinary teams developing, evaluating, and transitioning advanced capabilities. The role focuses on applying AI, machine learning, RF signal processing, and data science to national-security problems. Work may include preparing and analyzing RF and other sensor datasets; developing, training, and evaluating machine-learning models; implementing data- and signal-processing pipelines; conducting experiments; and assessing performance in realistic, noisy, and dynamic environments. The researcher will help translate AI research into usable prototypes through software development, model integration, testing and evaluation, documentation, demonstrations, and field experiments, and will contribute reports, presentations, and discussions with collaborators, sponsors, and operational stakeholders. This Associate Staff position is suitable for an early-career candidate with a technical foundation in AI, machine learning, computer science, physics, electrical engineering, or a related discipline and an interest in RF and cyber-electromagnetic applications. Associate Staff contribute independently to multidisciplinary work, support sponsor briefings, prepare test plans and reports, and develop creative solutions to technical problems. Depending on experience and assignment, they may own project components or lead workstreams and small teams. The work can span hardware and software development, systems and data analysis, component fabrication, integration, testing, and documentation. The Group offers mentorship, technical growth, hands-on experimentation, and exposure to the lifecycle of advanced technology development and transition. Candidates must be able to obtain and maintain a Secret-level DoD security clearance; U.S. citizenship is required.

Requirements

Bachelor’s degree in a STEM discipline or other relevant technical field and typically 2 or more years of relevant experience, or a master’s degree and typically no experience required, or an equivalent combination of education and experience for Level 3.
Bachelor’s degree in a STEM discipline or other relevant technical field and typically 4 or more years of relevant experience, or a master’s degree and typically 2 or more years of relevant experience; a PhD may be considered, or an equivalent combination of education and experience for Level 4.
Solid technical depth in a primary technical domain and ability to apply knowledge independently; Level 4 requires strong technical depth with emerging breadth across related disciplines or mission areas.
Demonstrated ability to solve complex technical problems and manage project components; Level 4 candidates should demonstrate ability to lead project components or technical workstreams.
Experience contributing to technical documentation, analyses, reports, presentations, or project deliverables.
Ability to collaborate across project teams and clearly communicate technical information to appropriate audiences.
Coursework, research, internship, project, or other demonstrated experience in artificial intelligence, machine learning, data science, statistical analysis, or signal processing.
Proficiency in Python and familiarity with scientific-computing and machine-learning libraries or frameworks such as NumPy, SciPy, pandas, PyTorch, TensorFlow, scikit-learn, or JAX.
Familiarity with core machine-learning concepts, including supervised and unsupervised learning, model training and validation, feature representation, performance evaluation, and overfitting mitigation.
Ability to analyze complex datasets, identify meaningful patterns, and communicate analytical findings clearly.
Ability to learn new technical areas rapidly and apply knowledge to challenging research and mission problems.
Clear and concise written and verbal communication of technical concepts, results, and recommendations.
Ability to work effectively as part of a multidisciplinary technical team.
Ability to obtain and maintain the security clearance required for the position; the posting specifies a Secret-level DoD clearance.
U.S. citizenship is required.

Nice To Haves

Deep learning, including convolutional, recurrent, transformer-based, generative, or self-supervised models.
Machine learning for time-series, sensor, communications, RF, or other high-dimensional scientific data.
RF machine learning, including modulation recognition, signal classification, anomaly detection, emitter identification, spectrum sensing, waveform analysis, or signal characterization.
Digital signal processing, including Fourier analysis, time-frequency methods, filtering, detection and estimation, communications, or statistical signal processing.
Applied electromagnetics, RF systems, antennas, wireless communications, radar, remote sensing, software-defined radio, or electronic warfare.
Data engineering, dataset curation, data labeling, data provenance, reproducible workflows, or large-scale data processing.
Model evaluation and AI assurance, including robustness, uncertainty quantification, explainability, domain adaptation, distribution shift, adversarial machine learning, or trustworthy AI.
Software-engineering practices, including version control, automated testing, continuous integration, containerization, Linux development, or collaborative software development.
Experience using high-performance computing, cloud computing, graphical processing units, or distributed training systems.
Experimental research, instrumentation, test and measurement, laboratory work, or field data collection.
Cyber operations, protocol analysis, embedded-system security, cyber-physical systems, network security, or cyber mission engineering.
Technical writing, research publications, project documentation, or presentations to technical audiences.

Responsibilities

Own or lead design, development, data analysis, programming, modeling, simulation, integration, testing, troubleshooting, and technical evaluation activities within broader project or program efforts.
Apply technical depth and judgment to complex, less-defined problems by selecting methods, adapting established approaches, and evaluating trade-offs.
Contribute to or lead system-, subsystem-, or component-level work spanning multiple disciplines, mission needs, products, or technical focus areas.
Prepare and analyze RF and other sensor datasets; develop, train, and evaluate machine-learning models; and implement data-processing and signal-processing pipelines.
Conduct experiments and assess model performance in realistic, noisy, and dynamic operating environments.
Support software development, model integration, test and evaluation, documentation, demonstrations, and field experiments to transition research concepts into prototype capabilities.
Develop, review, and integrate technical documentation, analyses, design materials, reports, presentations, papers, and other deliverables.
Coordinate milestones, assumptions, dependencies, quality expectations, and risks, and present findings, recommendations, and alternatives to appropriate audiences.
Communicate and collaborate with technical teams, sponsors, operational users, acquisition organizations, and other partners.
Provide technical guidance, review, feedback, and mentorship while developing mission-aligned expertise.
Participate in prototype development, laboratory tests, field demonstrations, and technology-transition activities.