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About This Role
Riverside Overview: Riverside Research is an independent National Security Nonprofit dedicated to research and development in the national interest. We provide high-end technical services, research and development, and prototype solutions to some of the country’s most challenging technical problems. All Riverside Research opportunities require U.S. Citizenship. Position Overview:
The computer systems we interact with on a daily basis are inherently flawed. By nature, many systems contain bugs and vulnerabilities. What if we could architect systems that think differently? The Secure and Resilient Systems group at Riverside Research aims to do just that. By leveraging secure input handling, model-based systems engineering (MBSE), and open architectures, we aim to ensure systems are secure by design, eliminating entire classes of vulnerabilities.
Riverside Research is seeking a dynamic and growth-focused junior-level research scientist to support research and development of bleeding-edge engineering security technologies. As a key member of our Secure and Resilient Systems team, the research scientist will interface with government research organizations (e.g. DARPA, IARPA, service labs, etc.), work on existing R&D contracts, and develop bleeding-edge technologies for transition to the warfighter. The research scientist will interface with team members across Riverside Research locations.
The research scientist will contribute to a diverse team responsible for developing security technologies in a variety of DoD systems. They will get hands-on experience working towards and developing secure parsers, MBSE artifacts and tools, and other technologies in the interest of DoD partners. They will develop software and systems that use new and existing technologies in areas that these technologies may have never been applied to. They will also contribute to technical writing in their research area.
The junior research scientist should have hands on experience in C/C++ and Python, as well as a strong understanding of the pitfalls of improper input handling. They should be able to think creatively to express why vulnerabilities such as Heartbleed can be classified as parser vulnerabilities. The research scientist should have experience working with one or more of the following: network protocols, MBSE tools, secure input handling, and open architectures. Responsibilities:
- Help the group design innovative parser and data protocol capabilities
- Assist in utilizing created and existing tools for DoD use cases
- Build new tools and/or capabilities in languages like C/C++, Python, etc.
- Contribute to whitepapers, proposals, and/or publish papers that document innovative work performed
- Collaborate with team members on debugging programs, designing systems, reviewing papers, etc.
- Participate in relevant internal and customer meetings
Qualifications:
Required Qualifications:* A deep technical understanding of cybersecurity problems and solutions
- Prior experience with memory safety vulnerabilities
- Proficiency in programming languages C/C++ and Python
- Experience with version control (Git)
- Knowledge on data format construction and an interest in exploring what makes a secure data format
- Knowledge of networking protocols
- Self-driven, strong analytic, inferencing, critical thinking, and creative problem-solving skills
- Able to obtain a clearance as needed
Desired Qualifications:* Bachelor's degree in Computer Science, Computer Engineering, Electrical Engineering, or Cybersecurity and 0-2 years of relevant experience
- Experience with parser design/development
- Experience with MBSE tools
- Healthy balance between technical orientation and business acumen
- Ability to operate independently with limited supervision and feedback, and establish a solid working relationship with technical staff and peers in the group and across Riverside Research
- Superior written and verbal communication skills
Global Comp: $60,000 - $85,000 This represents the typical compensation range for this position based on experience, location and other factors. Closing Statement: Riverside Research Institute is a not-for-profit, technology-oriented defense company, where service to our customers and support of our staff is our overall mission. Riverside is an affirmative action-equal opportunity employer and complies with all applicable federal, state, and local laws regarding recruitment and hiring. Riverside offers comprehensive compensation and benefit packages to our employees. Riverside bases its employment decisions solely on technical experience, qualifications and other job-related criteria related to our organizational purpose as a not-for-profit company, and without regard to race, color, religion, age, sex marital status, sexual orientation, national origin, physical or mental disability, veteran’s status or any other status legally protected by applicable federal, state, and local law.
Salary Context
This $60K-$85K range is in the lower quartile for Research Scientist roles in our dataset (median: $196K across 100 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 11,520 AI roles we're tracking, Research Scientist positions make up 2% of the market. At Riverside Research, 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 789 positions with disclosed compensation. Entry-level AI roles across all categories have a median of $112,320. This role's midpoint ($72K) sits 68% below the category median. Disclosed range: $60K to $85K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
Riverside Research AI Hiring
Riverside Research has 2 open AI roles right now. They're hiring across Research Scientist, Research Engineer. Positions span Beavercreek, OH, US, Dayton, OH, US. Compensation range: $85K - $145K.
Location Context
Across all AI roles, 6% (708 positions) offer remote work, while 10,737 require on-site attendance. Top AI hiring metros: New York (3,257 roles, $221,000 median); San Francisco (2,766 roles, $260,550 median); Los Angeles (1,580 roles, $210,000 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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, and the 90th percentile reaches $318,400. 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 Safety roles lead at $266,050 median, while Prompt Engineer roles sit at $140,000. 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 postings), Kubernetes (559 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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