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Help Explain Why Bridges Fail — And Design the Concrete That Won't
Roughly 600,000 bridges make up the National Highway System, and the concrete research happening in this lab directly shapes the design specifications engineers nationwide rely on to build and rate every one of them. When a bridge does fail, this is also the lab the National Transportation Safety Board and state DOTs call in to find out why. If you want research work with that kind of direct, visible impact — not just a report that sits on a shelf — this role is built for you.
Genex Systems is hiring a Concrete Research Engineer to plan and conduct structural research projects for the Federal Highway Administration (FHWA) in the Structures Laboratory at the Turner\-Fairbank Highway Research Center (TFHRC), the nation's premier highway research facility.
About the Lab
The Structures Laboratory specializes in developing and testing innovative bridge designs, materials, and construction methods aimed at making the nation's bridges more efficient and durable. Its work spans full\-scale testing of bridges and bridge components, field evaluation of in\-service structures, and forensic investigation of structural failures for state Departments of Transportation, FHWA divisions, and the National Transportation Safety Board (NTSB) — determining root causes of bridge failures and developing practices to prevent them from happening again.
What You'll Do
- Plan and conduct structural research projects focused on concrete as used in the design, rating, and construction of bridges
- Select and wire instrumentation for data acquisition — including strain gauges, accelerometers, tiltmeters, potentiometers, linear variable differential transducers (LVDTs), extensometers, and load cells
- Set up and operate closed\-loop, servovalve\-controlled hydraulic testing systems
- Design loading structures to support research test setups
- Conduct standard concrete property characterization tests, including compressive strength (ASTM C39\), flexural strength (ASTM C78\), modulus of elasticity (ASTM C469\), splitting tensile strength (ASTM C496\), creep (ASTM C512\), ultra\-high performance concrete fabrication and testing (ASTM C1856\), length change (ASTM C157\), and freeze\-thaw resistance (ASTM C666\)
- Write publication\-ready research reports, and present findings at demonstration projects, seminars, committee meetings, and workshops
What You'll Need (Required)
- Master's degree in Civil Engineering or Structural Engineering, plus at least 4 years of related research experience (or a Bachelor's Degree, plus 8 years of related research experience)
Nice to Have (Preferred)
- Ph.D. in Civil Engineering or Structural Engineering
- Experience with concrete as used in the design, rating, and construction of bridges
- Familiarity with digital\-to\-analog data acquisition and sensor selection/wiring (see instrumentation list above)
- Experience with setup and operation of closed\-loop, servovalve\-controlled hydraulic systems
- Experience with design of loading structures
- Familiarity with standard concrete property characterization tests (ASTM C39, C78, C469, C496, C512, C1856, C157, and C666\)
- Excellent oral and written communication skills, including the ability to write publication\-ready research reports and present at demonstration projects, seminars, committee meetings, and workshops
Compensation \& Benefits
Salary Range: $80,000–$124,000/ year, commensurate with education and experience.
Genex Systems offers a comprehensive benefits package, including:
- 401(k) retirement plan with employer matching
- Comprehensive medical, dental, and vision insurance
- Life insurance
- Generous Paid Time Off (PTO) and paid federal holidays
Location \& Work Arrangement
McLean, VA — full\-time, on\-site at the Turner\-Fairbank Highway Research Center.
Additional Information
Work Authorization/Citizenship: Company will sponsor work visa for qualified candidates including Cap\-Exempt H\-1B.
Genex Systems, LLC is an equal opportunity employer. Genex Systems does not discriminate in employment on account of race, color, religion, national origin, citizenship status, ancestry, age, sex, gender identity, sexual orientation, marital status, physical or mental disability, protected veteran status, or any other characteristic protected by federal, state, or local law.
Salary Context
This $80K-$124K range is in the lower quartile for Research Engineer roles in our dataset (median: $207K across 63 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 4,317 AI roles we're tracking, Research Engineer positions make up 2% of the market. At Genex Systems, 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 in Demand for This Role
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 $272,100 based on 227 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($102K) sits 63% below the category median. Disclosed range: $80K to $124K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and AI Engineering Manager ($244,000). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Genex Systems AI Hiring
Genex Systems has 1 open AI role right now. They're hiring across Research Engineer. Based in McLean, VA, US. Compensation range: $124K - $124K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 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.
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