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About This Role
NOV is seeking an NDT Physicist / NDT Research Engineer to support the development, validation, optimization, and application of advanced nondestructive testing technologies for industrial and tubular inspection applications.
The ideal candidate will have a strong technical foundation in ultrasonic testing (UT), phased array ultrasonic testing (PAUT), and electromagnetic inspection (EMI), combined with a bachelor’s degree or higher in Physics, Engineering, Materials Science, or a related technical discipline.
This is a hands-on technical individual contributor role focused on inspection technology, experimental testing, data analysis, troubleshooting, method development, and engineering problem solving.
- Develop, evaluate, and improve NDT inspection methods and technologies for industrial and tubular applications.
- Apply Ultrasonic Testing (UT), Phased Array Ultrasonic Testing (PAUT), and Electromagnetic Inspection (EMI) techniques to inspection and characterization challenges.
- Support development and optimization of inspection procedures, scan plans, calibration approaches, and inspection methodologies.
- Analyze NDT and inspection data to identify defects, characterize indications, evaluate inspection quality, and improve detection performance.
- Support testing and validation of inspection equipment, sensors, probes, transducers, and related instrumentation.
- Conduct laboratory and field testing to evaluate inspection system performance, repeatability, sensitivity, and reliability.
- Troubleshoot technical issues involving inspection equipment, acquisition systems, instrumentation, and inspection workflows.
- Apply engineering and physics principles to solve inspection and measurement problems.
- Use data analysis, modeling, simulation, or programming tools to support NDT technology development and evaluation.
- Document test methods, technical findings, procedures, results, and recommendations in engineering reports and technical documentation.
- Work closely with mechanical, electrical, software, manufacturing, quality, and operations teams.
- Support technology development from concept and experimental validation through implementation and continuous improvement.
- Stay current with applicable NDT standards, customer requirements, and industry practices.
Required Qualifications
- 3–5 years of hands-on NDT experience.
- Experience with Ultrasonic Testing (UT).
- Experience with Phased Array Ultrasonic Testing (PAUT).
- Experience with Electromagnetic Inspection (EMI) or a directly applicable electromagnetic NDT technology.
- Bachelor’s degree or higher in Physics, Engineering, Materials Science, Metallurgical Engineering, or another closely related technical discipline.
- Experience interpreting and analyzing inspection data.
- Demonstrated troubleshooting and technical problem-solving ability.
- Ability to work hands-on with inspection equipment and technical systems.
- Strong written and verbal communication skills.
- Ability to work effectively with multidisciplinary engineering and technical teams.
Preferred Qualifications
- Experience with eddy current, electromagnetic, MFL, EMAT, guided wave, or other advanced electromagnetic inspection technologies.
- Experience with NDT inspection systems, sensors, transducers, instrumentation, or data acquisition.
- Experience developing or validating NDT procedures and inspection methodologies.
- Experience with tubular, OCTG, pipeline, manufacturing, aerospace, oil & gas, or other safety-critical inspection applications.
- Experience with MATLAB, Python, C/C++, LabVIEW, or similar technical computing/programming tools.
- Experience with signal processing, image processing, numerical modeling, finite-element analysis, or data analytics.
- Experience with OmniScan, BeamTool, Eddyfi, Zetec, Evident/Olympus, or similar NDT equipment/software.
- Familiarity with ASME, ASTM, API, ASNT, or related inspection standards.
- Research, product development, laboratory experimentation, or technology commercialization experience.
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 NOV, 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.
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.
NOV AI Hiring
NOV has 1 open AI role right now. They're hiring across Research Engineer. Based in Houston, TX, US.
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.
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