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About This Role
Job Description Summary
Organization's Summary Statement:
The University of Maryland (UMD) Maryland Autonomous Technologies Research Innovation and eXploration Lab (MATRIX Lab) is an autonomous systems research, development, and education center operated by the A. James Clark School of Engineering at the University of Maryland, located at the University System of Maryland at Southern Maryland (USMSM) in St. Mary's County. The lab conducts cutting-edge research in autonomous technologies in partnership with federal sponsors including the U.S. Navy, Office of Naval Research (ONR), Naval Air Warfare Center Aircraft Division (NAWCAD), as well as leading defense industry partners.
The Clark School of Engineering at the University of Maryland serves as a catalyst for high-quality research, innovation, and learning, preparing students to address 21st-century grand challenges in energy, environment, security, and human health. The Clark School is dedicated to transforming the engineering discipline, accelerating entrepreneurship, and converting research and learning into innovations that benefit society.
Position Overview:
The Assistant Research Engineer (Radio Frequency & Sensing Systems) conducts applied and translational research at the intersection of RF/microwave engineering, multi-domain sensing, and autonomous systems integration at the MATRIX Lab. Reporting to Lab leadership and working in close collaboration with University of Maryland faculty, this position leads and supports the development of RF-enabled communication, sensing, and electronic warfare-resilient capabilities across the Lab's primary mission domains, with an emphasis on leveraging and advancing MATRIX's physical research infrastructure. The position is a full-time research appointment suited for a researcher with a strong background in RF/microwave systems, low size, weight, and power (SWaP) electronics, and defense-relevant sensing who seeks to translate circuit- and system-level expertise into deployable autonomous platform capabilities. Please note this position is located at the MATRIX Lab facilities in California, MD.
Key Responsibilities:
Roles and responsibilities will vary by project and candidate expertise and may include:
- Leading research and experimental activities in the MATRIX Lab anechoic chamber, including antenna characterization, RF system performance evaluation, radar cross-section measurement, electromagnetic compatibility testing, and validation of communication and sensing payloads intended for autonomous platform integration.
- Developing and evaluating low-SWaP RF sensing and communication architectures, including wake-up receivers, millimeter-wave transceivers, radar front-ends, and passive or backscatter-based sensing systems, suitable for deployment on size-, weight-, and power-constrained aerial, maritime, and underwater autonomous vehicles.
- Designing and conducting multi-domain sensing experiments in the Omni-domain Autonomous Systems Integration Space (OASIS) and Hydrodynamics Lab, integrating RF, optical, acoustic, and inertial sensing modalities on autonomous platforms operating in GPS-denied and electromagnetically complex environments.
- Utilizing the Hydrodynamics Lab water tunnel and related facilities to characterize underwater acoustic and RF propagation, evaluate submerged sensor performance, and support the development of sensing and communication payloads for autonomous underwater vehicle applications.
- Developing resilient RF and electronic systems capable of operating reliably under environmental extremes, interference, jamming, or electronic warfare conditions relevant to contested maritime and aerial operational environments.
- Collaborating with interdisciplinary teams including UMD faculty, graduate and undergraduate students, defense industry partners, and Navy program stakeholders to integrate RF and sensing capabilities into broader autonomous systems architectures and mission workflows.
- Supporting proof-of-concept prototyping and transition of RF/sensing subsystems from bench-level demonstration through integration with autonomous platforms tested in MATRIX Lab facilities, translating laboratory results into sponsor-relevant deliverables.
- Preparing technical deliverables and briefings for government and industry stakeholders, translating complex hardware and systems-level research into clear, decision-relevant products such as reports, slide briefings, demonstration summaries, and white papers.
- Leading technical proposal writing and contributing to sponsored research proposals and technical work plans, including scoping technical approaches, estimating level of effort, identifying risks, and supporting engagement with federal sponsors such as ONR, NAWCAD, Air Force Research Laboratory, etc.
- Mentoring graduate and undergraduate student researchers, providing technical guidance on RF circuit design, antenna systems, experimental measurement techniques, and research communication.
Physical Demands:
This position involves a mix of office-based research computing and active hands-on laboratory work. The position requires the ability to work at a computer workstation for extended periods and to conduct sustained experimental work in the anechoic chamber, water tunnel, and OASIS facilities, including handling RF test equipment, antenna systems, and autonomous platform payloads. The position may require working in confined or specialized measurement environments and operating sensitive instrumentation. Occasional travel to partner facilities, Navy test sites, and technical conferences may be required.
Preferences
Technical Background and Expertise
- Depth in one or more of the following areas: RF/microwave circuit design, millimeter-wave or sub-THz systems, antenna design and characterization, radar systems, acoustic sensing, or underwater electromagnetic propagation, with demonstrated ability to connect circuit- or system-level work to platform-level integration challenges
- Familiarity with the theoretical foundations underlying RF and sensing systems, including electromagnetic theory, signal propagation, noise and interference modeling, and nonlinear circuit behavior, enabling principled design and troubleshooting across novel problem settings
- Experience working across the hardware development pipeline from simulation or board-level design through prototyping, measurement, and system integration on physical platforms
- Exposure to constraints specific to defense-relevant hardware development, such as environmental hardening, radiation tolerance, electromagnetic hardening, or export-controlled technology considerations
- Familiarity with heterogeneous integration, advanced packaging, or multi-chip module approaches for combining sensing, processing, and communication functions in compact form factors is a plus
Instrumentation and Laboratory Skills
- Comfort operating RF and microwave test and measurement equipment including spectrum analyzers, signal generators, and near-field or far-field antenna range instrumentation
- Experience designing and executing controlled measurement campaigns in anechoic chambers or shielded environments, including proper calibration, uncertainty quantification, and documentation practices
- Familiarity with acoustic measurement systems, hydrophones, or underwater sensing instrumentation is a plus
- Experience with tools for RF circuit simulation and layout (e.g., Cadence, Keysight ADS, ANSYS HFSS, or equivalent)
Research and Professional Skills
- Track record of translating research outputs into tangible artifacts, such as functional prototypes, transition-ready hardware, open-source design files, or technical data packages, beyond peer-reviewed publication alone
- Experience briefing hardware and systems-level technical work to non-technical or mixed audiences, including government program managers, operational end users, or acquisition stakeholders
- Demonstrated ability to scope and self-manage hardware research tasks with limited supervision, including managing procurement, vendor coordination, and lab safety requirements for specialized equipment
Domain and Mission Awareness
- General familiarity with the federal defense research and acquisition ecosystem and how university-based applied RF and sensing research connects to sponsor mission needs, technology transition pathways, and program objectives
- Awareness of relevant operational contexts for RF and sensing systems, including electronic warfare, GPS-denied navigation, multi-domain communications, and contested spectrum environments relevant to Navy and joint service missions
- Awareness of responsible research, export control (ITAR/EAR), and dual-use technology considerations applicable to defense-relevant RF and sensing hardware development
Licenses/Certifications:
NA
Minimum Qualifications
Education:
M.S. in Computer Science, Electrical Engineering, Aerospace Engineering, Mechanical Engineering, Robotics, or a closely related field.
A Ph.D. in Computer Science, Electrical Engineering, Aerospace Engineering, Mechanical Engineering, Robotics, or a closely related field is preferred.
Experience:
Demonstrated record of peer-reviewed publication or equivalent technical deliverable is required. Hands-on experience with RF measurement instrumentation and controlled laboratory environments is required.
Other:
- Ability to work independently and collaboratively within a multidisciplinary research team operating across concurrent sponsored projects with varying timelines and deliverable requirements
- Strong written and oral communication skills, including demonstrated ability to author technical reports, conference papers, and journal articles
- Ability to manage and prioritize research and laboratory tasks across multiple active projects with competing deadlines
- Commitment to responsible research conduct, laboratory safety, and sensitivity to a culturally and ethnically diverse academic and research community
- Must be eligible to work on U.S. government-sponsored defense research; U.S. citizenship or permanent residency preferred
Additional Job Details
Required Application Materials: Cover letter, CV/resume, List of reference s
Best Consideration Date: October 15, 2026
Posting Close Date: October 22, 2026
Open Until Filled: N/A
Financial Disclosure Required
No For more information on Financial Disclosure, please visit Maryland's State Ethics Commission website .
Department
ENGR-A. James Clark School of Engineering Worker Sub-Type
Faculty Regular Salary Range
$90,000 - $115,000 Benefits Summary
For more information on Regular Faculty benefits, select this link .
Background Checks
Offers of employment are contingent on completion of a background check. Information reported by the background check will not automatically disqualify anyone from employment. Before any adverse decision, the finalist will have an opportunity to provide information to the University regarding disclosable background check information. The University reserves the right to rescind the offer of employment or otherwise decline or terminate employment if the information reported by the background check is deemed incompatible with the position, regardless of when the background check is completed.
Employment Eligibility
The successful candidate must complete employment eligibility verification (on Form I-9) by presenting documents that establish identity and work authorization within the timeframe required by federal immigration law, and where applicable, to demonstrate renewed employment authorization. Failure to complete employment eligibility verification or reverification within the timeframe set forth by law may result in suspension or termination of employment.
EEO Statement
The University of Maryland, College Park is an Equal Opportunity Employer. All qualified applicants will receive equal consideration for employment. Please read the University’s Equal Employment Opportunity Statement of Policy.
Title IX Non-Discrimination Notice
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Salary Context
This $90K-$115K range is in the lower quartile for Research Engineer roles in our dataset (median: $192K across 50 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 9,653 AI roles we're tracking, Research Engineer positions make up 2% of the market. At University of Maryland University College, 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 419 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($102K) sits 59% below the category median. Disclosed range: $90K to $115K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and AI Engineering Manager ($251,622). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
University of Maryland University College AI Hiring
University of Maryland University College has 2 open AI roles right now. They're hiring across Research Engineer, Research Scientist. Based in College Park, MD, US. Compensation range: $115K - $180K.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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