Aumovio
Automotive
ArtificialIntelligenceIntern
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“Artificial Intelligence Intern at Aumovio. Skills: AI Model Development, Embedded AI, Resource-Constrained Environments, Optimization Methodologies, Edge Deployment, Autonomous Driving, LLM Deployment. Develop efficient AI models for deployment in resource-constrained automotive environments.. Explore and implement optimization methodologies for AI models intended for embedded devices and edge platforms.”
What You'll Achieve.
Contribute to AI solutions that support smarter, safer, and more autonomous vehicles.
Industry & Context.
What They're Looking For.
Must Have
Currently pursuing a bachelor’s or master’s degree in programmes such as: Computer Science, Artificial Intelligence / Machine Learning, Computer / Electrical Engineering, or a related field., Proficiency in Python, Hands-on experience with machine learning projects (personal, academic, or open-source).
Nice to Have
Coursework in AI, Deep Learning, or Computer Vision is highly recommended., Familiarity with deep learning frameworks like PyTorch, TensorFlow, or scikit-learn., Hands-on experience with compiling and deploying code onto hardware targets such as SBCs like a Raspberry Pi, or microcontrollers.
What You'll Do.
Develop efficient AI models for deployment in resource-constrained automotive environments.
Explore and implement optimization methodologies for AI models intended for embedded devices and edge platforms.
and improve AI models for real-world automotive use cases
including autonomous driving
and intelligent cockpit applications.
Investigate methods for deploying lightweight Small Language Models on edge devices for use cases such as car cabin controls and automotive conversational assistants.
Evaluate the performance
and deployment feasibility of developed AI models through testing
and comparison with existing approaches.
Research and analyze the latest AI advancements from top-tier conferences
with a focus on efficient AI models
and improve state-of-the-art AI methods for practical automotive use cases.
or use existing AI models for resource-constrained environments.
Leverage cloud-based model development pipelines to support AI model development
Apply model optimization techniques to improve performance
and suitability for embedded devices.
Explore and implement methods for deploying lightweight Small Language Models on the edge for automotive cockpit applications.
Contribute to AI-powered autonomous driving and safety system applications.
and test AI models or related software on embedded hardware platforms where applicable.
Conduct experiments and evaluations to assess model performance
and deployment readiness.
How You'll Work.
Team & Collaboration
Collaborate with AI experts in Singapore and across the globe.; Presentations to communicate project outcomes to team members and stakeholders.
Communication Scope
Ability to clearly document findings; Present results to team members; Verbal and written communication skills
Full Job Description
Since its spin-off in September 2025 AUMOVIO continues the business of the former Continental group sector Automotive as an independent company. The technology and electronics company offers a wide-ranging portfolio that makes mobility safe, exciting, connected, and autonomous. This includes sensor solutions, displays, braking and comfort systems as well as comprehensive expertise in software, architecture platforms, and assistance systems for software-defined vehicles. In the fiscal year 2024 the business areas, which now belong to AUMOVIO, generated sales of 19.6 billion Euro. The company is headquartered in Frankfurt, Germany and has about 87.000 employees in more than 100 locations worldwide. This internship focuses on Developing Efficient AI Models in Resource-Constrained Environments, with exciting applications such as: • Autonomous Driving & Safety Systems – Contribute to AI-powered self-driving, assisted parking, and advanced safety features like emergency braking and fallback systems. • LLM-based Interactive Cockpit Systems – Develop methods and applications to deploy lightweight Small Language Models on the edge for use-cases such as car cabin controls, or automotive conversational assistants. Primary Goals: • Develop efficient AI models for deployment in resource-constrained automotive environments. • Explore and implement optimization methodologies for AI models intended for embedded devices and edge platforms. • Build, modify, benchmark, and improve AI models for real-world automotive use cases, including autonomous driving, and intelligent cockpit applications. • Investigate methods for deploying lightweight Small Language Models on edge devices for use cases such as car cabin controls and automotive conversational assistants. • Evaluate the performance, efficiency, and deployment feasibility of developed AI models through testing, benchmarking, and comparison with existing approaches. • Document project findings, implementation details, evaluation res
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