STR
Lead Research Scientist - Machine Learning (Clearance Required)
$174K–$220K
Woburn, MA +1 · Full-time · Posted May 5, 2026
- Location
- Woburn, MA
Woburn, Massachusetts, United States - Experience
- MS with at least 8 years of experience, and/or PhD with at least 5 years of experience (or equivalent experience).
- Education
- MS or PhD in a scientific field such as applied math, physics, electrical engineering, computer science, or data science; or equivalent experience.
- Visa sponsorship
- Active Top Secret clearance with SCI eligibility is required; U.S. citizenship is needed by the U.S. government.
About the job
Leads research and development of AI/ML algorithms for signals exploitation, estimation, system resource management, and related national security applications.
What you'll do
- Develop AI/ML algorithms for novel application domains and modalities.
- Lead or participate in project teams and interact with customers.
- Explore datasets and develop algorithmic techniques to solve customer problems.
What they ask for
- Active Top Secret clearance with SCI eligibility; U.S. citizenship is required by the U.S. government.
- MS with at least 8 years of experience, and/or PhD with at least 5 years of experience, or equivalent experience, in a scientific field.
- Experience building neural networks, adapting machine-learning approaches to new data and problems, and using standard data-science tools.
- Proficiency in Python or C/C++; ability to collaborate on and lead multidisciplinary teams and communicate technical concepts to varied audiences.
Nice to have
- Experience in intelligence or military-related mission areas.
- Experience applying deep learning beyond images/text, with self-supervised learning, or with time-series, geospatial, or spatio-temporal data.
Pay
$174,000 - $220,000 USD base salary
Tools
PyTorch, JAX, TensorFlow, scikit-learn, Pandas, Matplotlib, Python, C/C++
AI focus
Developing AI/ML algorithms for signals exploitation, estimation, resource management, and novel application domains; adapting machine-learning methods to new datasets and problems.
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