Interested in this Research Engineer role at Riverside Research?
Apply Now →Skills & Technologies
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:
Riverside Research is seeking an mid-level Embedded Hardware Security Research Engineer to work alongside our US Air Force (USAF) and Department of Defense (DoD) partners to develop tamper-protected microelectronic devices and intellectual property (IP). This position is an mid-level engineering role that provides a technical path and opportunity to become a Subject Matter Expert (SME) role in the understanding of advanced attacks against state-of-the-art integrated circuits and the design and development of protective controls to mitigate such attacks. You will work with nationally recognized hardware security experts employing a range of microelectronics test equipment, specialized software, and computing resources to provide exceptional support to our customers. This research work is conducted on-site in Riverside Research’s microelectronics and embedded systems security laboratory in Beavercreek, Ohio. Responsibilities:
- Performs research supporting assessment of state-of-the-art attack effectiveness against various microelectronic devices.
- Supports development of hardware algorithms for secure microelectronic devices (microcontrollers, FPGAs, etc.).
- Analyzes secure architectures to identify vulnerabilities.
- Develops, maintains, adjusts, and reports on project status and timelines.
- Collaborates with Government research leads to develop and present papers across a variety of DoD conferences and technical interchanges.
- Supports Principal Investigator and Program Manager in the development and documentation of technical requirements for approved research areas.
- Interacts with third-party vendors to collect appropriate source information as requested by program, leadership.
- Documents findings in technical reports and presentations.
- Develops and presents technical briefings to technical audiences and conferences.
Qualifications: Required:* Bachelor’s degree in Electrical Engineering, Computer Engineering, Cybersecurity Engineering, Physics, or Mathematics, and 5 years direct experience, through courses or previous employment, or a Master's degree and 3 years direct experience or a PhD without experience.
- Extensive experience developing IP with Verilog, VHDL, or SystemC.
- Previous experience programming microcontrollers or microprocessors.
- Proven knowledge on the design of security features of FPGAs or ASICs.
- Fluency in programming and/or scripting in C, C++, Rust, or Python.
- Previous experience in one or more of the following topics: side channel analysis, physically unclonable functions, reverse engineering, supply chain security, voltage or clock glitching.
- Experience performing statistical analysis and performing data analysis.
- Extensive experience using lab equipment (oscilloscopes, power supplies, etc.).
- Proven ability to work semi-independently on assigned tasks with limited scope and with technical supervision in a highly collaborative team-based R&D environment.
- Experience in creating technical reports, developing research papers and presentations supporting research areas.
- Willing to travel a few times per year.
- An active Secret clearance is required to start and a Top Secret clearance and additional program approvals must be obtained and maintained for this position.
Desired:* PhD in Electrical Engineering, Computer Engineering, Cybersecurity Engineering, Physics, or Mathematics
- Active security clearance.
- Previous experience applying microelectronics to one or more of the following areas: Signal Processing, RF, Secure Communications, Machine Learning, Software Verification, or Cybersecurity
- Understanding of cryptographic algorithms and protocols, or their implementation in embedded technologies including microcontrollers, FPGAs, ASICs.
- Familiarity implementing hardware attacks including side-channel, glitching, and their countermeasures.
- Familiarity using Virtual Machines (VirtualBox, VMWare, WSL, etc.), Linux OS, and other virtualization techniques
- Understanding of sub-100 nm CMOS and FinFET device fabrication, assembly, and packaging including 3DICs.
Global Comp: $100,000-$145,000 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 $100K-$145K range is below the median for Research Engineer roles in our dataset (median: $165K across 57 roles with salary data).
View full Research Engineer salary data →Role Details
About This Role
Research Engineers bridge the gap between research and production. They implement papers, build experiment infrastructure, optimize training pipelines, and make research prototypes production-ready. They're the engineers who make research work at scale.
The role sits at a unique intersection. You need to understand the math well enough to implement novel architectures correctly, and you need the engineering chops to make them run efficiently on distributed systems. When a research scientist has a breakthrough idea, you're the person who turns it from a notebook prototype into a training pipeline that runs on 256 GPUs.
Across the 9,079 AI roles we're tracking, Research Engineer positions make up 2% of the market. At Riverside Research, this role fits into their broader AI and engineering organization.
Research Engineer roles are growing as AI labs recognize that research velocity depends on engineering quality. The role is less competitive than Research Scientist (no PhD required), but the bar for engineering skill is very high. These roles are concentrated at major labs and well-funded startups.
What the Work Looks Like
A typical week involves: implementing a new attention mechanism from a recent paper, profiling and optimizing a training pipeline that's bottlenecked on data loading, building evaluation infrastructure for a new benchmark, debugging distributed training issues across a GPU cluster, and pair-programming with a research scientist on their latest experiment. The work is deeply technical.
Research Engineer roles are growing as AI labs recognize that research velocity depends on engineering quality. The role is less competitive than Research Scientist (no PhD required), but the bar for engineering skill is very high. These roles are concentrated at major labs and well-funded startups.
Skills Required
Strong software engineering fundamentals plus ML knowledge. Python, C++, and CUDA experience are common requirements. You'll need to read papers and turn ideas into working code. Distributed systems experience (especially distributed training) is highly valued. Performance optimization skills separate great candidates from good ones.
Experience with large-scale training infrastructure (FSDP, DeepSpeed, Megatron), GPU programming (CUDA, Triton), and the internals of ML frameworks (PyTorch internals, custom autograd functions) is what makes candidates stand out. The best research engineers can debug issues that span the full stack from GPU memory management to numerical precision to algorithmic correctness.
Strong postings mention the team's recent research, the infrastructure scale, and the specific technical challenges. They often list the research areas you'd support. Look for roles that emphasize both implementation quality and research understanding.
Compensation Benchmarks
Research Engineer roles pay a median of $252,000 based on 457 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($122K) sits 51% below the category median. Disclosed range: $100K to $145K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and AI Engineering Manager ($251,622). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Riverside Research AI Hiring
Riverside Research has 1 open AI role right now. They're hiring across Research Engineer. Based in Dayton, OH, US. Compensation range: $145K - $145K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 median).
Career Path
Common paths into Research Engineer roles include Software Engineer, ML Engineer, Research Intern.
From here, career progression typically leads toward Senior Research Engineer, Research Scientist, ML Architect.
This is one of the best entry points into AI research without a PhD. Build a strong engineering portfolio with ML projects, contribute to open-source ML frameworks, and demonstrate that you can implement complex ideas correctly and efficiently. The transition to Research Scientist is possible with published first-author work, which some research engineer roles support.
What to Expect in Interviews
Technical screens test both engineering skill and research understanding. Expect coding rounds with performance-critical implementations (GPU optimization, efficient data loading). Be prepared to discuss papers relevant to the team's research area and explain how you'd implement key ideas. System design questions focus on training infrastructure: distributed training, experiment tracking, and compute resource management.
When evaluating opportunities: Strong postings mention the team's recent research, the infrastructure scale, and the specific technical challenges. They often list the research areas you'd support. Look for roles that emphasize both implementation quality and research understanding.
AI Hiring Overview
The AI job market has 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 roles).
Research Engineer roles are growing as AI labs recognize that research velocity depends on engineering quality. The role is less competitive than Research Scientist (no PhD required), but the bar for engineering skill is very high. These roles are concentrated at major labs and well-funded startups.
The AI Job Market Today
The AI job market spans 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, 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 $256,000 median, while Prompt Engineer roles sit at $141,440. 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.