Senior Research Scientist

Northeastern University•Burlington, MA

9d•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. This role involves working as part of a multi-disciplinary team to execute R&D projects, focusing on providing technical contributions as a software engineer for projects involving machine learning (ML) and artificial intelligence (AI), including autonomy, sensing and communication, and decision support systems. The R&D Engineer will collaborate with academic and industry partners across the KRI consortium to create solutions and prototypes for applications in autonomous systems, robotics, cognitive and distributed sensing, and machine learning systems. The successful candidate will be a responsible team player, passionate about machine learning technologies, with a deep understanding of ML and 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 will be required. KRI was founded with a focus on homeland security R&D and now strives to advance resilience across a wide range of technologies, leveraging university intellectual capital to develop application-specific solutions. KRI emphasizes a collaborative approach and co-locates diverse R&D teams to address full-spectrum problems. KRI headquarters at NU Innovation Campus in Burlington, MA, features unique research and test facilities for RF signal processing, machine learning, unmanned and autonomous systems, and quantum materials and sensing. This position is with KRI at Northeastern University, LLC, a wholly-owned subsidiary of NU, primarily located at NU’s Innovation Campus at Burlington (ICBM).

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 workflow.
Demonstrated experience designing, implementing, and testing end‑to‑end ML/AI and/or simulation‑driven software systems, from data ingestion and model development to experimentation and 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), 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, system 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 developing or integrating simulation‑based models, including physics‑based, network‑based, agent‑based, or stochastic simulations for system analysis, experimentation, or decision support.
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.
Experience with modeling and simulation techniques, such as: Network, agent‑based, or discrete‑event simulation, Monte Carlo or stochastic simulation methods, Simulation‑in‑the‑loop (SiL) or synthetic data generation to support ML training and evaluation.
Experience integrating simulation outputs with ML models, decision‑support systems, or analytical pipelines.
Deep understanding of PostgreSQL/PostGIS, geospatial analytics, and large‑scale spatiotemporal datasets.
Exposure to UI or frontend development for technical applications, dashboards, or analyst‑facing tools (e.g., Svelte, React, or similar frameworks).
Familiarity with MLOps practices, experiment tracking, and reproducible research pipelines.
Experience collaborating with multidisciplinary teams across research, engineering, and operational stakeholders.
Security clearance.

Responsibilities

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