Research Scientist

About the Opportunity

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.

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) and 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 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.

Desired Skills & Attributes

• 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 NetworkX; 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 PostgreSQL/PostGIS is a plus.
• Interest in UI or frontend development for technical or analyst-facing tools (e.g., Svelte, React, or similar frameworks).
• Familiarity with MLOps concepts, experiment tracking, or reproducible research pipelines.
• Experience working with multidisciplinary teams across research, engineering, and applied R&D environments.
• Active security clearance is a plus.

Key Responsibilities & Accountabilities

• Software R&D activities, including software development and implementation, prototype modeling & simulation, design, and experimentation. (45%)
• Test and validation of software systems and software for prototype deployment. (45%)
• Provide software development subject matter expertise across a diverse set of application areas and contribute to proposals, publications, whitepapers, etc. (10%)