About the Opportunity
JOB SUMMARY
The Defense Industrial Base Institute at Northeastern University (NU) – a rapidly growing defense focused institute that conducts cutting-edge applied R&D – is seeking a highly motivated, experienced and enthusiastic Jr. Data Researcher with expertise in applying methods/frameworks/systems that apply ML&AI. The Jr. Data Researcher is expected to work as part of a multi-disciplinary team (academia, industry and government) and contribute to the successful execution of federally funded projects/activities.
Responsibilities include providing technical support to Principal investigators or co-principal investigators as a Jr. Data Researcher for a wide range of projects and activities involving machine learning (ML) and artificial intelligence (AI), including autonomy, sensing and communication, and decision support systems, among others. The Jr. Data Researcher will work collaboratively with multi-disciplinary teams across the DIBI Higher Education Consortium, consisting of academic, government, industry partners, to recommend solutions or support institute leadership on prototypes for projects in application areas, including autonomous systems, robotics, and machine learning systems, among others. Specifically, the Jr. Data Researcher will help to create and modify on the back end the existing Navigator methodology; working with other data scientists and leadership across the institute.
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 systems into practical, state-of-the-art systems that can be enhanced for projects or activities as per federal agency SOW.. A close working relationship with and support of thinking creatively on application of Navigator frame work for government and industry contracts will be required.
The primary office for this position is located at NU’s Boston Campus Through NU, the institute 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.
MINIMUM QUALIFICATIONS
Required
- Bachelor’s or Master’s degree in Data science, 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.
Desired 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)
- 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:
- 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
- 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.
Key Responsibilities:
Support Research and Development efforts to scale Navigator Methodology applied to defense contracts (SIB),to include, development and implementation, prototype modeling & simulation, design, and experimentation. This supports the defense funding received that supports this role. (40%)
Test and validation of soAI/ML Tools for sprints, tasks, projects and deliverables; to include scaling of projects by suggesting tools and systems that enhance efficiency and support humans in their work. (40%)
Provide subject matter expertise across a diverse set of application areas and ad hoc requests that may come in from the sponsor. This may include attending meetings, visits, conferences and engagements with US Government, industry and academic partners. Research and support PI or CO-PIs with scoping project budgets as appropriate (Systems, tools) (10%)
Special Projects as assigned: Working closely with Managing Director, Founding Director and other core leadership members, help to scope proposals and opportunities. Working closely with Managing Director and Sr. Director, Strategy and Communications to make recommendations on systems build out for internal operations (10%)
Position Type
Data Planning and AnalysisAdditional Information
Northeastern University considers factors such as candidate work experience, education and skills when extending an offer.
Northeastern has a comprehensive benefits package for benefit eligible employees. This includes medical, vision, dental, paid time off, tuition assistance, wellness & life, retirement- as well as commuting & transportation. Visit https://hr.northeastern.edu/benefits/ for more information.
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.
Compensation Grade/Pay Type:
108SExpected Hiring Range:
$60,315.00 - $85,192.50With 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.
Skills Required
- Bachelor's or Master's degree in data science, 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
- 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 such as PyTorch, including model training, evaluation, and experimentation
- Familiarity with distributed or accelerated computing environments, including GPU-enabled systems or shared compute clusters
- Working knowledge of relational databases such as PostgreSQL and SQL
- Exposure to graph databases such as Neo4j, Memgraph, or similar
- Familiarity with cloud computing environments such as Azure, AWS, or GovCloud equivalents
- Solid software engineering fundamentals, including version control, modular code design, testing, documentation, and reproducibility
- Ability to rapidly prototype solutions and iterate toward robust implementations with senior-engineer guidance
- Ability to work independently and contribute to broader project objectives
- U.S. citizenship and ability to obtain and maintain a security clearance
- Master's degree focused on ML/AI, data-intensive systems, network science, optimization, or related areas
- Experience contributing to government, defense, or security-related R&D programs
- Familiarity with simulation-based models for analysis, experimentation, or decision support
- Exposure to retrieval-augmented generation, vector databases, embedding pipelines, or LLM-enabled systems
- Familiarity with network science, graph analytics, graph modeling, NetworkX, graph-based ML, or GNNs
- Experience with Monte Carlo or stochastic simulation methods
- Experience with synthetic data generation or simulation-in-the-loop workflows
- Exposure to geospatial data, spatiotemporal datasets, or PostgreSQL/PostGIS
- Interest in UI or frontend development using Svelte, React, or similar frameworks
- Familiarity with MLOps, experiment tracking, or reproducible research pipelines
- Experience working with multidisciplinary research, engineering, and applied R&D teams
Northeastern University Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Northeastern University and has not been reviewed or approved by Northeastern University.
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Leave & Time Off Breadth — Paid time off is described as extensive, including 22–26 vacation days, 12 sick days, 13 holidays, and paid parental leave for birth/adoption. Additional paid leave is also outlined, including up to 26 weeks of paid medical leave and up to 12 weeks of paid family leave for eligible employees.
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Retirement Support — Retirement support is positioned as a standout, with an employer contribution described as 10% when an employee contributes 5%, alongside immediate vesting once eligible. This is presented as unusually generous relative to typical employer retirement offerings.
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Parental & Family Support — Family-oriented benefits are emphasized through tuition assistance for employees and dependents and access to backup childcare and family-care resources. Tuition remission/discount structures are highlighted as a major value driver, especially for employees with children and for long-tenured staff.
Northeastern University Insights
What We Do
Founded in 1898, Northeastern is a global research university with a distinctive, experience-driven approach to education and discovery. The university is a leader in experiential learning, powered by the world’s most far-reaching cooperative education program. We integrate classroom study with opportunities for professional work, research, service, and global learning in more than 100 countries. The same spirit of collaboration guides a use-inspired research enterprise focused on solving global challenges in health, security, and sustainability. Northeastern offers a comprehensive array of undergraduate and graduate programs leading to degrees through the doctorate in nine colleges and schools, and select graduate programs at campuses in Boston, Charlotte, N.C., San Francisco Bay Area, Seattle, and Toronto. Campuses in Burlington, MA, and Nahant, MA, are home to research institutes for homeland security and coastal sustainability, respectively








