Sr Research Scientist

Northeastern University•Burlington, MA

14h•Onsite

About The Position

The Kostas Research Institute (KRI) at Northeastern University (NU) is seeking a highly motivated, experienced, and enthusiastic Research & Development (R&D) Engineer with expertise in ML&AI. The R&D Engineer will work as part of a multi-disciplinary team, contributing to the successful execution of R&D projects. Responsibilities include providing technical contributions as a software engineer for projects involving machine learning (ML) and artificial intelligence (AI), such as autonomy, sensing, communication, and decision support systems. The role involves collaboration with academic and industry partners within the KRI consortium to create solutions and prototypes for applications including autonomous systems, robotics, cognitive and distributed sensing, and machine learning systems. Successful candidates should be team players, passionate about machine learning technologies, possess a deep understanding of ML technology, and have experience in developing 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 is required. KRI was founded with a focus on homeland security R&D and now strives to advance resilience across a wide range of technologies, emphasizing a collaborative approach that leverages R1 university intellectual capital and technologies to develop application-specific solutions. KRI focuses on satisfying customer-driven needs by co-locating a diverse, highly skilled R&D team. KRI headquarters is located at NU Innovation Campus in Burlington, MA (ICBM), featuring research and test facilities for cognitive and distributed RF signal processing, machine learning, unmanned and autonomous system technologies, and quantum materials and sensing. This position is with KRI at Northeastern University, LLC, a wholly-owned subsidiary of NU, with the primary office at NU’s ICBM. KRI offers an impressive benefits package through NU, including multiple retirement plan options with generous matching and tuition waiver for classes and advanced degree programs.

Requirements

Bachelor's or Master's degree in Electrical Engineering, Computer Engineering, Computer Science, Applied Mathematics, or a closely related field.
5+ years of professional experience in software engineering with a strong focus on machine learning and AI systems development (research, applied R&D, or production environments).
Strong proficiency in Python and modern ML/AI development workflows.
Demonstrated experience designing, implementing, and testing end-to-end ML/AI software systems, from data ingestion to model deployment.
Hands-on experience with machine learning frameworks, particularly PyTorch, including model training, fine-tuning, evaluation, and experimentation.
Experience working in high-performance computing (HPC), distributed compute, or accelerated environments (GPUs, multi-node systems).
Solid background in database systems, including: Relational databases (e.g., PostgreSQL / SQL) and Graph databases (e.g., Neo4j, Memgraph, or equivalent).
Familiarity with cloud computing environments (e.g., Azure, AWS, or GovCloud equivalents), including containerized or scalable ML workflows.
Strong software engineering fundamentals: version control, modular design, testing, documentation, and reproducibility.
Proven ability to rapidly prototype novel solutions and transition them toward robust, deployable systems.
Self-motivated team member capable of contributing to technical planning, architecture decisions, and problem decomposition.
U.S. Citizenship with the ability to obtain and maintain a security clearance.

Nice To Haves

Advanced degree (M.S. or Ph.D.) with applied ML/AI, network science, optimization, or data-intensive systems focus.
Experience supporting government, defense, or security-related R&D programs.
Experience with C++ and/or Java for performance-critical components.
Experience with Retrieval-Augmented Generation (RAG) architectures, vector databases, embedding pipelines, and LLM-integrated systems.
Strong background in network science and graph analytics, including: Graph modeling and analysis using tools such as NetworkX.
Graph-based ML or graph neural networks (GNNs) is a plus.
Deep understanding of PostgreSQL/PostGIS, geospatial analytics, and large-scale spatiotemporal datasets.
Experience designing and integrating decision-support or analytical pipelines that combine ML, graph analytics, and domain data.
Exposure to UI or frontend development for technical applications, dashboards, or analyst-facing tools: Experience with Svelte, React, or similar modern frameworks is a plus.
Familiarity with ML model operationalization (MLOps), experiment tracking, and reproducible research pipelines.
Experience collaborating with multidisciplinary teams across research, engineering, and operational stakeholders.

Responsibilities

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.
Working 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.
Being responsible team players and passionate about machine learning technologies.
Possessing a deep understanding of machine learning technology and experience in turning machine learning technologies into practical, state-of-the-art systems.
Maintaining a close working relationship with and support of KRI Senior R&D Engineers/Scientists for government and industry contracts.