Applied AI/ML Researcher

MIT Lincoln Laboratory Associate Staff provide critical multi-domain and expert multi-domain innovative research supporting the Laboratory's national security mission. They prototype state-of-the-art technology solutions and engage in many steps of a program lifecycle with increasing independence and responsibility. As an Associate Staff member, you will contribute independently to multidisciplinary efforts, contribute to sponsor briefings, write test plans and reports, and apply complex and creative solutions to solving technical problems. You may own discrete project/program components or lead complex workstreams and small teams.

As a successful candidate, you will bring solid to strong technical depth, broaden your understanding across adjacent disciplines, and adapt approaches where precedent is limited. You will integrate technical inputs, contribute to program documentation and reporting, and collaborate with interdisciplinary project/program teams supporting projects that may range from rapid, high-risk efforts to longer-term mission programs. Your work will include multiple activities such as hardware and software development, systems analysis, data analysis, component fabrication, system integration, testing, and documentation.

Group 51 is seeking an Associate Staff member with a concentration in artificial intelligence and machine learning to support the development, evaluation, and transition of advanced Cyber-Electromagnetic Warfare capabilities. The successful candidate will work as part of multidisciplinary teams applying AI, machine learning, RF signal processing, and data science to challenging national-security problems.

The candidate will contribute to research and prototype development involving RF machine learning, including signal, protocol, and emitter identification and characterization. Responsibilities may include preparing and analyzing RF and other sensor datasets; developing, training, and evaluating machine-learning models; implementing data-processing and signal-processing pipelines; conducting experiments; and assessing model performance in realistic, noisy, and dynamic operating environments.

The candidate will work with experienced technical staff to translate AI research concepts into usable prototype capabilities. This may include supporting software development, model integration, test and evaluation, documentation, demonstrations, and field experiments. The candidate will also contribute to technical reports, presentations, and discussions with collaborators, sponsors, and operational stakeholders.

This position is well suited for an early-career candidate who has a strong foundation in AI, machine learning, computer science, physics, electrical engineering, or a related discipline; is interested in RF and cyber-electromagnetic applications; and is motivated to apply technical skills to high-impact national-security mission problems. The Group will provide opportunities for mentorship, technical growth, hands-on experimentation, and exposure to the full lifecycle of advanced technology development and transition.

• 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 less-defined and complex problems, identifying appropriate 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.
• Develop, review, and integrate technical documentation, analyses, design materials, program reporting, presentations, papers, and other deliverables.
• Coordinate milestones, assumptions, dependencies, quality expectations, and risks and present findings, recommendations, and alternatives to appropriate audiences.
• Provide technical guidance, review, feedback, and mentorship while continuing to deepen mission-aligned expertise.

The Associate Staff member will have opportunities to:

• Develop expertise in RF machine learning and AI applications for cyber-electromagnetic operations.
• Work with RF data, signal-processing systems, software-defined radios, simulation environments, and operationally relevant datasets.
• Participate in prototype development, laboratory tests, field demonstrations, and technology-transition activities.
• Receive mentorship from experts in AI, RF systems, physics, cyber operations, and electronic warfare.
• Contribute to technical reports, sponsor briefings, and research publications.
• Build an understanding of how advanced technical capabilities move from research through acquisition and operational deployment.

Candidates will be considered for the appropriate level based on their education, experience, demonstrated technical capabilities, leadership responsibilities, and the scope and complexity of their prior work.

Level 3 - Associate Staff

• Bachelor's degree in a STEM discipline or other relevant technical field and typically 2+ years of relevant experience; or Master's degree and typically 0+ years of relevant experience; or an equivalent combination of education and experience.
• Solid technical depth in a primary engineering, research, or other technical domain, with the ability to apply knowledge independently.
• Demonstrated ability to solve complex technical problems and manage components of project work.
• Experience contributing to technical documentation, analyses, reports, presentations, or project deliverables.
• Ability to collaborate across project teams and communicate technical information clearly to appropriate audiences.
• Emerging ability to guide less experienced colleagues and contribute to team capability development.

Level 4 - Associate Staff

• Bachelor's degree in a STEM discipline or other relevant technical field and typically 4+ years of relevant experience; or Master's degree and typically 2+ years of relevant experience; a PhD may be considered; or an equivalent combination of education and experience.
• Strong technical depth in a primary domain with emerging breadth across related engineering, research, technical disciplines, or mission areas.
• Demonstrated ability to solve complex technical problems and lead components of projects or technical workstreams.
• Experience managing milestones, dependencies, risks, and deliverables for assigned project areas.
• Ability to communicate complex information to varied audiences and collaborate with project teams, sponsors, and internal partners.
• Demonstrated ability to mentor others, review work, and contribute to team technical capability.

• 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 and frameworks, such as NumPy, SciPy, pandas, PyTorch, TensorFlow, scikit-learn, JAX, or similar tools.
• 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 effectively to challenging research and mission problems.
• Ability to communicate technical concepts, results, and recommendations clearly and concisely in written and verbal formats.
• Ability to work effectively as part of a multidisciplinary technical team.
• Ability to obtain and maintain the security clearance required for the position.

Experience or demonstrated interest in one or more of the following areas is desirable:

• 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.