Materials Informatics Jobs in Virginia (NOW HIRING)

Why choose between doing meaningful work and having a fulfilling life? At MITRE, you can have both. That's because MITRE people are committed to tackling our nation's toughest challenges-and we're committed to the long-term well-being of our employees. MITRE is different from most technology companies. We are a not-for-profit corporation chartered to work for the public interest, with no commercial conflicts to influence what we do. The R&D centers we operate for the government create lasting impact in fields as diverse as cybersecurity, healthcare, aviation, defense, and enterprise transformation. We're making a difference every day-working for a safer, healthier, and more secure nation and world. Our workplace reflects our values. We offer competitive benefits, exceptional professional development opportunities for career growth, and a culture of innovation that embraces adaptability, collaboration, technical excellence, and people in partnership. If this sounds like the choice you want to make, then choose MITRE - and make a difference with us. Department Summary: MITRE Technology and Engineering (MTE)'s Chemical and Optical Sciences Department serves as a vanguard for MITRE to meet the future needs of our customers, requiring technical excellence while encouraging creativity and fostering interests in current and emerging technologies across its diverse employees. Your future colleagues are a collegial team from diverse technical backgrounds who work across MITRE's sponsors and partners focused on the public good. Roles & Responsibilities: * Develop deep learning and classical machine learning (ML) models for applications in chemistry and material science
* Building ML pipelines and statistical analysis
* Partner with experimentalists to design and develop new ML algorithms
* Work in the public interest to provide novel materials solutions to our sponsors' most critical problems
* Be an independent contributor in laboratory experimentation efforts related to new materials fabrication, characterization, and analysis
* Be familiar with emerging materials concepts and be able to formulate experiments focused on validating the applicability of said concepts to a variety of different application/mission spaces
* Interface with sponsors to discuss their most critical technical problems
* Be able to propose novel materials-based ideas within MITRE's internal research and development program.
* As needed and desired, contribute to other technical tasks requiring physical science and engineering expertise, including, but not limited to, next-generation materials for microelectronics.
* Some domestic travel will be necessary. Basic Qualifications: * PhD with relevant experience who can immediately contribute at this job level
* Ph.D. in Chemistry, Materials Science, Chemical Engineering, Computational Chemistry, Materials Informatics, Physics, Computer Science or a closely related STEM discipline (or MS with additional experience).
* Proficiency in Python and data science core libraries NumPy, PyTorch, JAX, pandas scikit-learn
* Familiarity with software engineering best practices (version control, documentation) and open-source contributions
* Firm grasp of materials science and engineering, with an emphasis on polymer/elastomer composites.
* Demonstrated ability to communicate complex technical concepts to multidisciplinary teams and government sponsors.
* Experience leading independent research efforts and transitioning innovative concepts into operationally relevant capabilities.
* Strong systems thinking and ability to connect fundamental science with mission outcomes.
* The ability to obtain a Department of War SECRET clearance
* Per the U.S. Government's eligibility requirements, you must be a U.S Citizen to be considered for a security clearance.
* This position requires a minimum of 4 days a week on-site Preferred Qualifications: * Experience applying AI to laboratory automation, including autonomous experimentation, inverse design, or closed-loop optimization.
* Peer-reviewed publications applying AI/ML to chemistry and/or materials science
* Experience developing and applying deep learning methods for scientific discovery, including graph neural networks (GNNs), transformer architectures, diffusion models, flow matching models, and representation learning for molecular and materials systems.
* Understanding of symmetry-aware ML concepts relevant to materials science, including equivariant neural networks, geometric deep learning, and physics-informed or symmetry-preserving model architectures.
* Experience with computational chemistry and materials science python libraries, including one or more of:
* pymatgen
* Atomic Simulation Environment (ASE)
* RDKit
* matminer
* FireWorks
* scikit-learn
* PyTorch
* Familiarity with atomistic simulation methods, molecular modeling, density functional theory (DFT), molecular dynamics (MD), or high-throughput computational screening workflows.
* Experience with foundation models, large language models, scientific ML, AI agents, or autonomous research systems for scientific reasoning and discovery.
* Record of open-source software contributions, or successful transition of research into operational capabilities.
* Demonstrated success leading multidisciplinary teams spanning AI, chemistry, materials science, software engineering, robotics, and systems engineering.
* Experience identifying emerging technologies and shaping research strategies aligned with national security priorities.
* Passion for advancing trustworthy and responsible AI for scientific applications.
* Excellent written and verbal communication skills with technical and executive stakeholders
* Active or current DoD security clearance (Secret or higher). This requisition requires the candidate to have a minimum of the following clearance(s):
None This requisition requires the hired candidate to have or obtain, within one year from the date of hire, the following clearance(s):
Secret Salary compensation range and midpoint:
$143,600 - $179,500 - $215,400 Annual Work Location Type:
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