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Magna International

AI Robotics Intern- Summer 2026

internship•Novi

Summary

Location

Novi

Type

internship

Experience

0-2 years

Company links

WebsiteLinkedInLinkedIn

About this role

Job descriptions may display in multiple languages based on your language selection.

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:

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:

Position Overview

We’re looking for a graduate intern to help build Physical AI systems—AI that perceives, reasons, and acts in the real world (or high-fidelity simulation). You’ll work with a small team to prototype, train, and evaluate models for robotics tasks like manipulation, navigation, perception, motion planning, and sim-to-real transfer—and deliver working demos and measurable improvements.


Location: Windsor, Ontario or Detroit, Michigan (Travel to Windsor needed)

Essential Duties and Responsibilities:

  • Research latest tools and mechanisms for Physical AI development
  • Build and iterate on robot learning pipelines for embodied tasks (e.g., grasping, navigation, manipulation) in Pytorch.
  • Work in robotics simulation (e.g., Isaac Sim/Isaac Lab, MuJoCo, Gazebo) and help improve sim fidelity / physics / domain randomization.
  • Implement components in Go and/or C/C++, with attention to performance (parallelism, memory, throughput).
  • Evaluate systems on real or representative platforms (robot arms / AMRs / legged robots / sensors) and present results to the team.
  • Collaborate cross-functionally (software, ML, controls, hardware) to deliver a prototype that works end-to-end.

Qualifications:

  • Hands-on experience with Robotics or Deep-Learning with Pytorch
  • Bachelor’s in Electrical, Mechanical or Industrial Engineering
     

Education / Experience

  • Currently pursuing MS or PhD in EE, Computer Science, Robotics, ISE or related field.
     

Special Knowledge / Skills:

  • Strong programming skills in C/C++, AWS, CUDA programming
     

Physical Demands / Work Environment:

  • Normal amount of sitting or standing, average mobility to move around in lab environment, able to conduct normal amount of work at a computer.

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.

Notice regarding the use of AI:

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.

If you have any questions or concerns about this process, feel free to contact our Talent Attraction team.

Worker Type:

Student / Co-Op

Group:

Magna Corporate

What you'll do

  • The intern will help build Physical AI systems by prototyping, training, and evaluating models for robotics tasks. Responsibilities include researching tools for AI development and collaborating cross-functionally to deliver prototypes.

About Magna International

We see a future where everyone can live and move without limitations. That’s why we are developing technologies, systems and concepts that make vehicles safer and cleaner, while serving our communities, the planet and, above all, people. Forward. For all. Our common shares trade on the Toronto Stock Exchange (MG) and the New York Stock Exchange (MGA). For further information about Magna, visit www.magna.com.

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Frequently Asked Questions

What does a AI Robotics Intern- Summer 2026 do at Magna International?

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As a AI Robotics Intern- Summer 2026 at Magna International, you will: the intern will help build Physical AI systems by prototyping, training, and evaluating models for robotics tasks. Responsibilities include researching tools for AI development and collaborating cross-functionally to deliver prototypes..

Is the AI Robotics Intern- Summer 2026 position at Magna International remote?

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The AI Robotics Intern- Summer 2026 position at Magna International is based in Novi, Michigan, United States. Contact the company through Clera for specific work arrangement details.

How do I apply for the AI Robotics Intern- Summer 2026 position at Magna International?

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You can apply for the AI Robotics Intern- Summer 2026 position at Magna International directly through Clera. Click the "Apply Now" button above to start your application. Clera's AI-powered platform will help match your profile with this opportunity and guide you through the application process.
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