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About This Role
About the Opportunity
*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 .
Education \& Experience
Required
- 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.
*
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.
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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.
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Desired Skills \& Attributes
- 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.
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Key Responsibilities \& Accountabilities :
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%)
Position Type
Research
Additional 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:
110S
Expected Hiring Range:
$76,335\.00 \- $107,823\.75
*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.*
Salary Context
This $76K-$107K range is in the lower quartile for Research Scientist roles in our dataset (median: $196K across 93 roles with salary data).
Role Details
About This Role
Research Scientists push the boundaries of what AI can do. They design experiments, develop novel architectures, publish papers, and translate research breakthroughs into production capabilities. This is where the fundamental advances happen, from attention mechanisms to diffusion models to reasoning chains.
The work is intellectually demanding and often ambiguous. You might spend months on an approach that doesn't pan out. The best research scientists combine deep mathematical intuition with engineering pragmatism. They know when to go deep on theory and when to run experiments. They read papers voraciously and can spot incremental contributions from genuine breakthroughs.
Across the 3,824 AI roles we're tracking, Research Scientist positions make up 3% of the market. At Northeastern University, this role fits into their broader AI and engineering organization.
Research Scientist roles are concentrated at major AI labs (OpenAI, Anthropic, Google DeepMind, Meta FAIR) and well-funded AI startups. The competition is intense. PhD is effectively required for most positions, and publication track record matters. Compensation is among the highest in AI, reflecting both the scarcity of talent and the strategic importance of research breakthroughs.
What the Work Looks Like
A typical week includes: reading and discussing recent papers with your team, designing and running experiments on multi-GPU clusters, analyzing results and iterating on hypotheses, writing up findings for internal review or publication, and collaborating with engineering teams to productionize promising results. The ratio of thinking to coding is higher than in engineering roles.
Research Scientist roles are concentrated at major AI labs (OpenAI, Anthropic, Google DeepMind, Meta FAIR) and well-funded AI startups. The competition is intense. PhD is effectively required for most positions, and publication track record matters. Compensation is among the highest in AI, reflecting both the scarcity of talent and the strategic importance of research breakthroughs.
Skills Required
PhD strongly preferred for most roles. Deep expertise in a specific area (NLP, computer vision, reinforcement learning, multimodal) is expected. PyTorch is the standard. Publication track record matters. Strong mathematical foundations in linear algebra, probability, optimization, and information theory are assumed.
Beyond the fundamentals, companies value experience with large-scale distributed training, novel architecture design, and the ability to bridge theory and practice. Understanding of current frontier topics (reasoning, multimodal, long-context, alignment) is essential. Code quality matters more than many researchers expect. Labs want researchers who can implement their ideas cleanly.
Strong research postings specify the research area, mention the team you'd join, and describe the problems they're working on. They often list recent publications from the team. Vague 'AI research' postings without specifics usually mean the company wants to sound impressive but doesn't have a real research agenda.
Compensation Benchmarks
Research Scientist roles pay a median of $223,400 based on 223 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $160,000. This role's midpoint ($92K) sits 59% below the category median. Disclosed range: $76K to $107K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
Northeastern University AI Hiring
Northeastern University has 3 open AI roles right now. They're hiring across Research Scientist. Based in Burlington, MA, US. Compensation range: $107K - $165K.
Location Context
Across all AI roles, 16% (613 positions) offer remote work, while 3,187 require on-site attendance. Top AI hiring metros: New York (2,448 roles, $210,000 median); San Francisco (1,990 roles, $253,000 median); Los Angeles (1,686 roles, $189,000 median).
Career Path
Common paths into Research Scientist roles include PhD Student, Research Engineer, Postdoc.
From here, career progression typically leads toward Research Lead, Distinguished Scientist, VP of Research.
The PhD is the entry point for most paths. Choose your advisor and research area carefully since they'll define your first industry position. Publish consistently, contribute to open-source projects in your area, and build relationships at conferences. Industry research offers better compensation and compute resources than academia, but the pressure to show product impact is real.
What to Expect in Interviews
Research interviews are multi-stage: a research talk (present your best paper), technical deep-dives on your methodology, and often a 'research proposal' exercise where you design an experiment to test a hypothesis. Coding rounds test implementation ability alongside theoretical knowledge. Be prepared to implement a paper from scratch and discuss the design choices the authors made. Strong candidates can critique papers constructively and identify gaps in experimental methodology.
When evaluating opportunities: Strong research postings specify the research area, mention the team you'd join, and describe the problems they're working on. They often list recent publications from the team. Vague 'AI research' postings without specifics usually mean the company wants to sound impressive but doesn't have a real research agenda.
AI Hiring Overview
The AI job market has 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 roles).
Research Scientist roles are concentrated at major AI labs (OpenAI, Anthropic, Google DeepMind, Meta FAIR) and well-funded AI startups. The competition is intense. PhD is effectively required for most positions, and publication track record matters. Compensation is among the highest in AI, reflecting both the scarcity of talent and the strategic importance of research breakthroughs.
The AI Job Market Today
The AI job market spans 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (119) are outnumbered by mid-level (1,813) and senior (1,472) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,000. Top-quartile roles start at $253,000, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $142,800. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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