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About This Role
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What we offer:
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:
Cosma provides a comprehensive range of body, chassis and engineering solutions to global customers. Through our robust product engineering, outstanding tooling capabilities and diverse process expertise, we continue to bring lightweight and innovative products to market.Job Responsibilities:
Department: Information Technology
Job Level: Junior / Entry Level
Reports To: IT Operations Manager
Location: Hybrid / On-site as applicable
Position Summary
We are seeking a Junior AI Engineer to join our team and help design, develop, integrate, and support artificial intelligence solutions that improve business processes and employee productivity. The Junior AI Engineer will work alongside experienced engineers and IT teams to build process improvement applications using technologies such as: generative AI, large language models (LLMs), machine learning, APIs, automation, software applications and cloud services. This is an ideal opportunity for an early-career engineer who has a strong foundation in software development and wants to gain hands-on experience delivering secure, responsible, enterprise-scale process improvement software.
Key Responsibilities
- Assist with the design and development of AI-enabled applications, and automation solutions.
- Develop applications that integrate with LLMs, traditional machine learning models, and generative AI services.
- Build and maintain prompts, structured outputs, tool/function integrations, and AI workflows.
- Assist with implementing Retrieval-Augmented Generation (RAG) solutions using enterprise data sources.
- Evaluate model responses for accuracy, relevance, reliability, and overall user experience.
- Participate in prototyping and proof-of-concept development for new AI use cases.
Software Engineering & Integration
- Develop and maintain applications and services using Python, .NET, and other supported development languages.
- Build and consume REST APIs and integrate AI capabilities with existing enterprise applications.
- Work with structured and unstructured data from databases, documents, APIs, and business systems.
- Use source control and modern software development practices, including Git, CI/CD, testing, and code reviews.
- Assist with troubleshooting, debugging, performance optimization, and production support.
Quality, Security & Governance
- Follow organizational standards for Responsible AI, security, privacy, and data governance.
- Assist with implementing logging, monitoring, evaluation, and feedback mechanisms for applications.
- Ensure sensitive and confidential organizational information is handled appropriately.
- Integrate applications to function with existing CAD, FEA, and PDM software.
Collaboration & Continuous Learning
- Translate business requirements into technical prototypes and solutions with guidance from senior team members.
- Stay informed about developments in generative AI, machine learning, software engineering, and CAD/FEA automation.
- Share findings, demonstrations, documentation, and lessons learned with the broader technology organization.
Required Qualifications
- Bachelor's degree in Computer Science, Software Engineering, Data Science, Information Technology, related discipline, or equivalent practical experience.
- 0–2 years of professional software development, data, automation, or AI experience.
- Programming experience with Python.
- Understanding of software development fundamentals, including: + APIs + Data structures + Object-oriented programming + Source control/Git + Testing and debugging
- Basic understanding of machine learning, generative AI, and large language models.
- Familiarity with JSON, REST APIs, SQL, and common data formats.
- Strong analytical and problem-solving skills.
- Strong written and verbal communication skills.
- Ability and willingness to learn new technologies quickly.
Preferred Qualifications
Experience or academic/project exposure to one or more of the following is beneficial:
- Web development
- .NET / development
- CAD and FEA software integration
- AI agents and agentic workflows
- Microsoft Copilot Studio
- Prompt engineering
- Retrieval-Augmented Generation (RAG)
- Vector databases and semantic/vector search
- Semantic Kernel or similar AI orchestration frameworks
- Machine learning frameworks such as PyTorch or TensorFlow
- Docker and containerized applications
- GitHub
- CI/CD pipelines
- SQL and NoSQL databases
- Enterprise identity and security concepts
- Data analytics and visualization
Knowledge & Skills
The successful candidate should demonstrate:
- Curiosity: Strong interest in emerging technologies and experimentation.
- Engineering mindset: Ability to move beyond demonstrations and build maintainable solutions.
- Problem solving: Ability to break complex business problems into manageable technical components.
- Responsible AI awareness: Understanding that AI solutions require attention to security, privacy, bias, accuracy, and human oversight.
- Collaboration: Ability to work effectively across technical and business teams.
- Communication: Ability to explain technical concepts clearly to both technical and nontechnical audiences.
- Adaptability: Comfortable working in a rapidly evolving technology environment.
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.
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:
Regular / PermanentGroup:
Cosma International
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Magna International, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $212,040 based on 13,780 positions with disclosed compensation. Entry-level AI roles across all categories have a median of $112,320.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
Magna International AI Hiring
Magna International has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Troy, MI, US.
Location Context
Across all AI roles, 6% (708 positions) offer remote work, while 10,737 require on-site attendance. Top AI hiring metros: New York (3,257 roles, $221,000 median); San Francisco (2,766 roles, $260,550 median); Los Angeles (1,580 roles, $210,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, 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 $266,050 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 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.
Frequently Asked Questions
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