Interested in this Research Engineer role at DCS Corp?
Apply Now →Skills & Technologies
About This Role
ARCTOS a DCS company is seeking a highly motivated research scientist or engineer to join our team supporting the AFRL/RE Composite, Ceramic, Metallic & Materials Performance Division. This position will lead efforts to fill critical gaps in the understanding of the behavior of ceramic matrix composites and translate that knowledge into models to predict performance and durability in extreme hypersonic and space operational environments.
The successful candidate will operate at the intersection of experimental mechanics, advanced diagnostics, and multiscale computational modeling. Working alongside materials development, processing, and characterization scientists, this individual will spearhead the development of novel testing frameworks and non-traditional modeling paradigms that overcome the limitations of legacy methods.
Essential Job Functions:
The Research Engineer will collaborate with Government sponsors, research partners, and multi-disciplinary technical teams in the performance of the following duties:
Formulate and implement advanced computational modeling strategies that move beyond classical continuum homogenization to account for thin-ply features, architectural complexity, and lack of length-scale separation.
Develop and execute or guide novel mechanical testing methodologies tailored to complex CMC architectures, moving beyond legacy standards derived from metals and traditional polymer composites.
Correlate high-resolution material characterization data (e.g., X-ray computed tomography, microstructural quantification) directly with finite element models and failure predictions.
Establish progressive design and life-prediction methodologies that replace overly conservative, empirical knockdowns with physics-based, risk-quantified design margins.
Collaborate with materials processing teams to assess how emerging manufacturing pathways influence defect distributions, geometric variation, and component structural integrity.
Present findings at technical conferences, author technical reports and peer-reviewed publications, and brief senior stakeholders on technical progress and strategic impact.
Required Skills:
Due to the sensitivity of customer related requirements, U.S. Citizenship is required.
Able to obtain and maintain a U.S. Department of War Secret security clearance.
M.S. or Ph.D. in Materials Science and Engineering, Mechanical Engineering, Aerospace Engineering, Engineering Mechanics, or a related technical field.
4 years of experience supporting engineering analysis, scientific computing, digital engineering, experimental thermomechanical testing, or computational mechanics is highly desirable.
Experience supporting DoW, aerospace, or other configuration-controlled engineering programs is highly desirable.
The well-qualified candidate will have expertise in several of the following areas:
Computational Modeling & Mechanics of Materials
Solid foundation in continuum mechanics, fracture mechanics, and damage mechanics.
Experience developing finite element analysis (FEA) models for heterogeneous or architected materials using commercial solvers (Abaqus, ANSYS, COMSOL) or research codes.
Expertise in mesoscale modeling, discrete-damage modeling, or multiscale mechanics.
Scripting and scientific computing proficiency (Python, MATLAB, C++, or Fortran) for automated model generation, data reduction, and scientific visualization.
Mechanical Testing & Advanced Characterization
Hands-on experience developing and/or executing thermomechanical material tests for extreme environments (thermal gradients, complex multiaxial stress states), preferably for ceramics or CMCs.
Expertise in full-field strain measurement techniques (2D/3D DIC).
In-depth understanding of non-destructive evaluation (NDE) and advanced characterization methods (e.g., micro-CT, synchrotron X-ray imaging, SEM/EDS, acoustic emission).
Design Methodologies & Systems Context
Familiarity with hypersonic thermal protection systems (TPS) or hot-structure concepts.
Appreciation for uncertainty quantification (UQ), sensitivity analysis, and probabilistic design frameworks.
Understanding of how component geometry, manufacturing defects, and processing variability influence structural performance and certification.
Desired Skills:
The successful candidate should be:
An agile, interdisciplinary thinker able to bridge the gap between hands-on laboratory experimentation and advanced computational mechanics.
Enthusiastic in tackling unconventional problems that lack established textbook solutions.
A strong communicator capable of articulating complex physical phenomena clearly to experimentalists, computational modelers, and program leadership.
Self-motivated and comfortable driving research projects forward in collaborative, rapidly evolving R&D environments.
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 DCS Corp, 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.
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.
DCS Corp AI Hiring
DCS Corp has 1 open AI role right now. They're hiring across Research Engineer. Based in Beavercreek, OH, US.
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.