AI Engineer Intern

Job Description

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

The Tone:
This is a internship at Rivian and Volkswagen Group Technologies, located in Palo Alto, CA or Irvine, CA. The company is a joint venture between two industry leaders with a clear vision for automotive’s next chapter, developing technology for software-defined vehicles around the world, from operating systems to cloud and connectivity solutions. This role contributes to research and development across speech, vision, language, and multimodal AI systems, offering hands-on experience across the full AI development lifecycle to build scalable, robust, and efficient models for conversational and in-vehicle applications.

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

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

What You’ll Actually Do
• Contribute: Research and develop generative and discriminative AI models for speech, vision, and multimodal systems, including ASR, TTS, speech-to-speech, speaker modeling, video understanding, self-supervised learning, and diffusion-based generative models.
• Train & Evaluate: Supervise fine-tuning and evaluate deep learning and foundation models using large, domain-specific datasets.
• Apply Techniques: Utilize reinforcement and generative policy learning techniques such as imitation learning, preference-based optimization, and alignment methods like RLHF, RLAIF, and DPO.
• Manage Data: Curate, preprocess, and manage multimodal datasets, including audio, image, video, text, and preference or trajectory data.
• Experiment & Document: Design and execute experiments to explore new modeling, generative, and alignment approaches, collaborating with cross-functional teams and clearly documenting workflows and results.

The Must-Haves
• Background: Currently pursuing an MS or PhD in Computer Science, Machine Learning, Electrical Engineering, or a related field.
• Experience: Strong Python programming skills and practical experience with PyTorch or TensorFlow.
• Skills: Solid understanding of machine learning, deep learning, generative modeling, and reinforcement learning fundamentals.
• Bonus: Hands-on experience with diffusion models, SFT, reinforcement learning, imitation learning, or preference-based optimization; experience with multimodal or foundation models; exposure to distributed training or large-scale inference systems; contributions to research projects, publications, or open-source software; familiarity with transformer-based architectures, diffusion models, or modern speech/video/language modeling approaches; experience using AI-assisted development tools.