Research Scientist

About the role

This job description is intended to describe the general nature and level of work being performed by people assigned to this classification. It is not intended to be construed as an exhaustive list of all responsibilities, duties and skills required of personnel so classified . Job Summary: The Kostas Research Institute (KRI) at Northeastern University (NU) - a rapidly growing institute that conducts cutting-edge applied R&D - is seeking a highly motivated, experienced and enthusiastic Research & Development (R&D) Engineer with expertise in ML&AI. The R&D Engineer is expected to work as part of a multi-disciplinary team and contribute to the successful execution of R&D projects. Responsibilities include providing technical contributions as a software engineer for a wide range of projects involving machine learning (ML) and artificial intelligence (AI), including autonomy, sensing and communication, and decision support systems, among others. The R&D Engineer will work collaboratively with multi-disciplinary teams across the KRI consortium, consisting of academic and industry partners, to create solutions and prototypes for projects in application areas, including autonomous systems, robotics, cognitive and distributed sensing, and machine learning systems, among others. Successful candidates will be responsible team players and passionate about machine learning technologies, as well as possess a deep understanding of machine learning technology and experience in turning machine learning technologies into practical, state-of-the-art systems. A close working relationship with and support of KRI Senior R&D Engineers/Scientists for government and industry contracts will be required. The Kostas Research Institute was founded with a focus on homeland security research and development. Today, KRI strives to advance resilience in the face of 21st century risks across a wide range of technologies, emphasizing a collaborative approach that leverages our R1 university intellectual capital and technologies to develop application-specific solutions to customer needs. KRI focuses on satisfying customer-driven needs by co-locating a diverse, highly skilled R&D team that can address all aspects of a particular problem across the full range of technology-readiness levels. KRI headquarters, located at the NU Innovation Campus in Burlington, MA (ICBM), is home to one-of-a-kind research and test facilities for conducting activities related to cognitive and distributed RF signal processing and machine learning, unmanned and autonomous system technologies, as well as quantum materials and sensing. This position is with KRI at Northeastern University, LLC, a wholly-owned subsidiary of NU. The primary office for this position is located at NU's ICBM. Through NU, KRI offers an impressive benefits package, including multiple retirement plan options with extremely generous matching, as well as tuition waiver for classes and advanced degree programs. A full description of available benefits can be found on the NU website . Education & Experience Required Bachelor's or Master's degree in Electrical Engineering, Computer Engineering, Computer Science, Applied Mathematics, or a closely related field. 2-4 years of professional experience in software engineering, data science, or applied R&D, with exposure to machine learning and AI system development in research, prototype, or production environments. Preferred Master's degree with a focus on ML/AI, data ‑ intensive systems, network science, optimization, or related areas. Experience contributing to government, defense, or security ‑ related R&D programs (internships, fellowships, or full ‑ time roles). Familiarity with simulation ‑ based models (e.g., physics ‑ based, network ‑ based, agent ‑ based, or stochastic simulations) for analysis, experimentation, or decision support. Skills & Attributes Required Proficiency in Python and familiarity with modern ML/AI development workflows. Exposure to C++ and/or Java for performance ‑ critical components is a plus. Experience contributing to the design, implementation, testing, or evaluation of ML/AI ‑ enabled or simulation ‑ driven software systems. Hands ‑ on experience with machine learning frameworks (e.g., PyTorch), including model training, evaluation, and experimentation. Familiarity with distributed or accelerated computing environments (e.g., GPU ‑ enabled systems, shared compute clusters). Working knowledge of database systems, including: Relational databases (e.g., PostgreSQL / SQL) Exposure to graph databases (e.g., Neo4j, Memgraph, or similar) Familiarity with cloud computing environments (e.g., Azure, AWS, or GovCloud equivalents), including containerized or scalable ML workflows. Solid software engineering fundamentals, including version control, modular code design, testing, documentation, and reproducibility. Ability to rapidly prototype solutions and iterate toward more robust implementations wit