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About This Role
Essential Functions
- Lead welding projects, review welding specifications and develop optimal welding practices for commercial and branded steel, and overlay products in accordance with industry standards and codes (AWS, ASME, API, etc.), or internal specifications. Assists in the design, execution and reporting of product/process development and/or quality improvement trials
- Oversee R\&D’s welding, thermal cutting and ultrasonic inspection equipment
- Interact with plant operating and technical services personnel on project or technical service activities (planning, execution and reporting). Assist with maintaining the welder qualification program at SSAB mills in North America.
- Review engineering designs for welding requirements and manage weldability testing and welding qualifications for commercial and branded plate products.
- Interpret data and provide recommendations to Operations or Commercial management with limited guidance from supervisor or team leader. Provide training for operations welding personnel
- Conduct failure analysis of welded components
- Provide consulting services and support customers with welding related issues
- Participate in weld\-focused technical groups and professional organizations both internally and externally
- Prepare clear and concise written reports as required. May present technical papers within and outside the company
- Provide technical direction and supervision to research technicians and/or coop students working on welding related projects
- Review and interpret nondestructive examination (NDE) procedures and specifications. Establish working procedures for conducting NDE inspections utilizing conventional ultrasonic inspection and phased\-array.
Education, Experience, and Competencies
Required:
- MSc. in Welding or Mechanical Engineering or related discipline
- Strong technical writing and organizational skills
- Strong analytical capabilities
- Good written and verbal communication skills
- Good computer skills
Preferred:
- PhD in Welding or Mechanical Engineering or a related discipline
- Background in material science
- Professional Engineer or Certified Welding Engineer (CWE) licensed welding engineer
- Good knowledge of metallurgy, welding metallurgy and weldability testing of steel
- Hands\-on experience with arc welding processes (GMAW, GTAW, FCAW, SMAW, SAW, etc.)
- Experience with welding of dissimilar materials and ultra\-high strength steels
- Experience in using metallographic lab equipment and processes, including sample preparation, optical (LOM) and scanning electron microscopy (SEM), energy dispersive spectroscopy (EDS), and electron backscatter diffraction (EBSD)
- Experience with mechanical testing and characterization of weldments, including bend testing, cross weld tensile testing, HAZ CVN testing, microhardness testing, chemical etching, and weldability testing methods to determine susceptibility to hydrogen cracking
- Experience in non\-destructive testing (NDT), including ultrasonic testing (UT), dye\-penetrant testing (PT), and magnetic particle testing (MT)
- Experience with industry codes and standards, including but not limited to AWS, ASME, ASTM, API, ABS, Lloyds, EN/ISO
Supervisory Responsibilities/Direct Reports
This position has no supervisory responsibilities but must maintain a close working relationship with R\&D staff and other personnel.
Work Environment and Physical Demands
The employee will frequently be required to:
- This position operates in an office environment. The role routinely uses standard office equipment such as computers, network tools/equipment, phones, photocopiers and scanners. This role is partially sedentary (average of 6 hours per day), however, some standing and walking is required.
- The position requires working with laboratory equipment such as optical microscopes, SEM/EDS/EBSD, welding, bend testing, polishing machines, hardness testing, tensile testing, CVN testing, DWTT machine, band saws, thermal cutting, waterjet cutting, surface grinder, heat treating furnaces, and crane.
- The position requires preparing various acid solutions for etching steel specimens such as sulfuric acid, picric acid, oxalic, nital etc.
The employee will occasionally be required to:
- This position will periodically require work in the mill and in the field environment with some exposure to heat/cold, walking, climbing stairs/ladders, noise and wearing safety equipment/clothing for protection from hazards
- Some handling of materials is required. This would require the ability to walk, bend, kneel or stand, use of hands to finger, hold and handle, reach with hands and arms and to push/pull, lift or carry items up to 50 lbs.
- Drive or travel by airplane
The position requires the following vision and hearing capabilities:
- Talking person to person, on the phone, and/or in group settings
- Hearing in person, on the phone, and/or in group settings
- Vision at near, mid, and far range
Position Type – Work Hours \- Travel Requirements
- Position is full time, exempt and works regular day shift Monday through Friday – additional hours as needed
- Travel 25% of time in the field or at other SSAB locations
Other Duties
Please note this job description is not designed to cover or contain a comprehensive listing of activities, duties or responsibilities that may be required of the employee for this job. Duties, responsibilities and activities may change at any time with or without notice.
Our Offer
*SSAB BENEFITS – starting at hire!* Excellent Pay with Additional Bonus Very Affordable Health Insuranceü Blue Cross/Blue Shield Medical
ü CVS/Caremark Prescription
ü Delta Dental
ü Superior Vision
401(k) with Employer Match – 5% Profit Sharing 10 Paid Holidays Vacation Company Paid Life Insurance \- and STD and LTD\* Optional Life, Hospital Indemnity, Critical Illness Healthcare and Dependent Care Flexible Spending Account (FSA) Tuition Reimbursement Employee Assistance Program
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 3,708 AI roles we're tracking, Research Engineer positions make up 2% of the market. At SSAB, 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 $280,000 based on 147 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and AI Architect ($254,798). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
SSAB AI Hiring
SSAB has 1 open AI role right now. They're hiring across Research Engineer. Based in Montpelier, IA, US.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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