Research Scientist

Northeastern University•Burlington, MA

9h•Onsite

About The Position

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.

Requirements

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.
Proficiency in Python and familiarity with modern ML/AI development workflows.
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)
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.

Nice To Haves

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.
Exposure to C++ and/or Java for performance ‑ critical components is a plus.
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.
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.

Responsibilities

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%)