Research Scientist

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.

• 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.

• 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), graph databases (e.g., Neo4j, Memgraph, or similar).
• Familiarity with cloud computing environments (e.g., Azure, AWS, or Gov Cloud 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 with guidance from senior engineers.
• Self‑motivated team member able to work independently on well‑defined tasks while contributing to broader project objectives.
• U.S. Citizenship with the ability to obtain and maintain a security clearance.

• Exposure to Retrieval‑Augmented Generation (RAG), vector databases, embedding pipelines, or LLM‑enabled systems.
• Familiarity with network science or graph analytics concepts, including graph modeling and analysis using tools such as Network
  
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• Introductory experience with graph‑based ML or GNNs is a plus.
• Experience or coursework involving modeling and simulation techniques, such as network, agent‑based, or discrete‑event simulation, Monte Carlo or stochastic simulation methods.
• Synthetic data generation or simulation‑in‑the‑loop workflows.
• Exposure to geospatial data, spatiotemporal datasets, or…