AI Engineer Intern

Job Description

Data Engineering Intern – AI & Analytics (Fall 2026) | Rivian and Volkswagen Group Technologies

The Tone:
This is an internship at Rivian and Volkswagen Group Technologies, located in Palo Alto, CA. This joint venture between industry leaders is focused on developing core automotive technologies, including operating systems, zonal controllers, and cloud and connectivity solutions, to define the next generation of software-defined electric vehicles. The role is crucial for advancing AI systems for conversational and in-vehicle applications, offering hands-on experience across the entire AI development lifecycle. You will contribute to building scalable, robust, and efficient models that will shape the future of intelligent automotive features.

The TL;DR
• Role: Internship
• Type: Full-time
• Location: In-person, Palo Alto, CA

• Team: Data, AI & Analytics department
• Mission: Contribute to research and development across speech, vision, language, and multimodal AI systems, working to build, train, evaluate, and deploy advanced models for conversational and in-vehicle applications.
• Tech Stack: Python, PyTorch, TensorFlow

What You’ll Actually Do
• Develop: Contribute to the research and development of generative and discriminative AI models for speech, vision, language, and multimodal systems.
• Train & Evaluate: Supervised fine-tune (SFT) and evaluate deep learning and foundation models on large, domain-specific datasets.
• Implement: Apply reinforcement and generative policy learning techniques, including imitation learning, preference-based optimization, and alignment methods.
• Manage Data: Curate, preprocess, and manage multimodal datasets, encompassing audio, image, video, text, and preference or trajectory data.
• Experiment: Design and execute experiments to explore new modeling, generative, and alignment approaches.

The Must-Haves
• Background: Currently pursuing an MS or PhD in Computer Science, Machine Learning, Electrical Engineering, or a related field at a US university.
• Experience: Strong Python programming skills and experience with PyTorch or TensorFlow, alongside a solid understanding of machine learning, deep learning, generative modeling, and reinforcement learning fundamentals.
• Skills: Python programming, PyTorch/TensorFlow, Machine Learning, Deep Learning, Generative Modeling, Reinforcement Learning.
• Bonus: Hands-on experience with diffusion models, SFT, reinforcement learning, imitation learning, or preference-based optimization; experience working with multimodal or foundation models; or exposure to distributed training or large-scale inference systems.