Sr Research Scientist

• 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.
• Experience with C++ and/or Java for performance-critical components is a plus.
• 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)
• 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.

• 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 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.

Northeastern University considers factors such as candidate work experience, education and skills when extending an offer.  

All qualified applicants are encouraged to apply and will receive consideration for employment without regard to race, religion, color, national origin, age, sex, sexual orientation, disability status, or any other characteristic protected by applicable law.

With the pay range(s) shown above, the starting salary will depend on several factors, which may include your education, experience, location, knowledge and expertise, and skills as well as a pay comparison to similarly-situated employees already in the role. Salary ranges are reviewed regularly and are subject to change.