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What we offer:
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At Magna, you can expect an engaging and dynamic environment where you can help to develop industry\-leading automotive technologies. We invest in our employees, providing them with the support and resources they need to succeed. As a member of our global team, you can expect exciting, varied responsibilities as well as a wide range of development prospects. Because we believe that your career path should be as unique as you are.Group Summary:
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Magna is more than one of the world’s largest suppliers in the automotive space. We are a mobility technology company built to innovate, with a global, entrepreneurial\-minded team. With 65\+ years of expertise, our ecosystem of interconnected products combined with our complete vehicle expertise uniquely positions us to advance mobility in an expanded transportation landscape.Job Responsibilities:
Role Summary:
The Senior Research Engineer/Advanced Engineering will work on technology projects within the Corporate R\&D Advanced Body Domain, supporting the technical evaluation and due diligence of new developments in multiple
areas of the complete vehicle, including body, exteriors, seating, mechatronics, vehicle integration, and other system\-level engineering and product development.
The Senior Research Engineer/Advanced Engineering will plan, execute, and coordinate all related activities for/with all other internal Corporate R\&D Domains, Magna Group R\&D Departments, as well as external Supplier. companies, institutions, and academia as needed.
Key Responsibilities:
- Contribute to the development of new technologies and/or product and/or system ideas, either adding content per vehicle (CPV) – top line (revenue) and/or improving profitability (bottom line)
- Analyze automotive market trends along with region\-specific regulations and develop domain\-specific, futuristic, two\-generational product and technology roadmap, in collaboration with Magna groups. (body, structures, exteriors, seating, and mechatronics)
- Collaborate with Magna groups' R\&D teams to identify and develop product and process gaps to improve CPV or reduce product costs.
- Initiate new R\&D projects by developing problem statements and value propositions.
- Execute end–to–end Proof of Technology (PoT) or Proof of Concept (PoC) using Ideation Development Process (IDP)
- Provide input into project prioritization based on total addressable, relevant market analysis, working with other Magna R\&D and business development groups.
- Conduct advanced development studies to support program timing and budget goals.
- Participate in the ideation process while identifying gaps in the current product or manufacturing technology, to guide and provide future R\&D efforts to improve the top and/or bottom line.
- Perform deep dive analyses on current problem statements (product and process), status quo (materials, design, process, BOM, DVP\&R, D\-FMEA, etc.), and its cost drivers or technology gaps.
- Analyze engineering results and propose product changes to determine the feasibility and improvement of components, systems, and functional/performance specifications.
- Analyze customer requirements and develop KPIs for new products or technologies in alignment with the Magna group/s.
- Plan and lead IDP gate reviews, and related cross\-functional efforts with internal/external customers with required documentation (example – DVP\&R, D\-FMEA, KPI, etc.) defined within IDP.
- Confer with other Research Engineers to clarify or resolve problems and develop design solutions.
- Collaborate with CAD Design Engineers and Designers for the generation of 2D/3D CAD data.
- Continue to innovate on behalf of internal and external customers.
- Perform other duties as required.
Education:
- Bachelor of Science degree in Mechanical Engineering or Industrial Engineering ; Master’s degree in Mechanical Engineering or Industrial Engineering preferred.
- Knowledge of manufacturing processes and assembly methods
Experience:
- 5\+ years of recent experience working either as a Researcher, Technical Lead, Product /System Engineer in body / exterior / seating/ mechatronics, and their integration into various systems.
- Experience working with body, structures, exterior, seating, and mechatronics and their integration into various systems
- Experience working with Teamcentre to review CAD, cut sections, and present (preferred)
Required Skills
- Excellent collaborator seeking WIN\-WIN scenario.
- Proficiency in Microsoft Office (Word, Excel, PowerPoint, Teams)
- Technical ability and creativity to initiate, direct, and control specific projects.
- Strong written and verbal communication skills
- High Self\-motivation, Never Settle, and Be\-Curious (15\-Why) thinking.
- Ability to influence without authority and collaborate effectively across teams.
Health \& Safety
- Ensure all Company policies, procedures, plant standards, and safety rules are consistently adhered to.
- Display a positive attitude towards safety, work safely, and follow all the Company's safety policies and procedures.
- Comply with all Health, Safety and Environmental standards in accordance with the Magna Employee Charter, the Occupational Health \& Safety Act, and the Michigan Occupational Health \& Safety Act.
- Ensure all employees and visitors have a safe environment in that safety and housekeeping standards are maintained; and all hazards, unsafe actions, or unsafe conditions have been identified, corrected, and/or eliminated.
- Respond to and investigate all reports of injuries, accidents, illnesses, near misses and property damage immediately.
- Take an active part in the office/division’s safety program by performing regular workplace inspections, effectively investigate accidents/incidents promptly and assists in meeting or exceeding the office/division’s safety goals.
Awareness, Unity, Empowerment: At Magna, we believe that a diverse workforce is critical to our success. That’s why we are proud to be an equal opportunity employer. We hire on the basis of experience and qualifications, and in consideration of job requirements, regardless of, in particular, color, ancestry, religion, gender, origin, sexual orientation, age, citizenship, marital status, disability or gender identity. Magna takes the privacy of your personal information seriously. We discourage you from sending applications via email or traditional mail to comply with GDPR requirements and your local Data Privacy Law.
Awareness, Unity, Empowerment:
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At Magna, we believe that a diverse workforce is critical to our success. That’s why we are proud to be an equal opportunity employer. We hire on the basis of experience and qualifications, and in consideration of job requirements, regardless of, in particular, color, ancestry, religion, gender, origin, sexual orientation, age, citizenship, marital status, disability or gender identity. Magna takes the privacy of your personal information seriously. We discourage you from sending applications via email or traditional mail to comply with GDPR requirements and your local Data Privacy Law.
AI\-Assisted Screening Disclosure
As part of our commitment to a fair, consistent, and efficient recruitment process, we may use artificial intelligence (AI) tools to assist in the initial screening of applications submitted through our Workday system. These tools help identify qualifications and experience that align with the role requirements. Please note that AI is used solely to support our recruiters. Final decisions are always made by the hiring manager and the hiring team. Importantly, no applicant data is shared externally through these AI tools. All information remains securely within our systems and is handled in accordance with our privacy and data protection policies.
Under conditions defined by applicable law, you may have the right to request an explanation of how AI is used to support decision\-making.
If you have any questions or concerns about this process, feel free to contact our Talent Attraction team.
Worker Type:
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Regular / PermanentGroup:
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Magna Corporate
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 3,824 AI roles we're tracking, Research Engineer positions make up 2% of the market. At Magna International, 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 $260,000 based on 401 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
Magna International AI Hiring
Magna International has 1 open AI role right now. They're hiring across Research Engineer. Based in Troy, MI, US.
Location Context
Across all AI roles, 16% (613 positions) offer remote work, while 3,187 require on-site attendance. Top AI hiring metros: New York (2,448 roles, $210,000 median); San Francisco (1,990 roles, $253,000 median); Los Angeles (1,686 roles, $189,000 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 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 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 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). 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 (119) are outnumbered by mid-level (1,813) and senior (1,472) 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 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 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 $200,000. Top-quartile roles start at $253,000, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $142,800. 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 (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 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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