Fall Intern – AI/Machine Learning Research

Job Description

2026 Fall Intern, ML/NLP Research | Samsung Research America (SRA)

The Tone:
This is a Fall Internship at Samsung Research America (SRA), located in Mountain View, CA. The AI Research Center (AIC) within SRA focuses on research and development that directly impacts future Samsung products used by hundreds of millions of users worldwide, specifically pushing the state-of-the-art in natural language and knowledge intelligence. This role is crucial for students who thrive in a fast-paced, results-driven environment, contributing to highly visible and challenging cross-discipline projects. You will be part of an exciting project to build an adaptive, personalized, contextual, and secure AI model and system, enabling fast, accurate, and safe interactions tailored to users’ needs on Samsung devices.

The TL;DR
• Role: Internship
• Type: Temporary (3 months)
• Location: In-person, Mountain View, CA
• Pay: $48—$63 hourly
• Team: AI Research Center (AIC)
• Mission: To build an adaptive, personalized, contextual, and secure AI model and system for fast, accurate, and safe user interactions on Samsung devices.
• Tech Stack: PyTorch, TensorFlow

What You’ll Actually Do
• Algorithm Development: Develop and implement novel deep learning or reinforcement learning algorithms specifically for natural language processing, including both text and speech, across various applications.
• Research Contribution: Actively contribute to the ongoing research activities within the team.
• Patent Generation: Generate creative solutions leading to potential patents.
• Publication: Publish research findings in top-tier conferences.

The Must-Haves
• Background: Current Ph.D. student in Computer Science, Electrical Engineering, or a related field.
• Experience: Expertise in LLM (model architecture, training/finetuning, RAG, reasoning, action planning), or planning/tool use/agent AI/agent memory for autonomous systems, or knowledge augmented AI (language prompt, knowledge graph, neuro-symbolic learning), or conversational AI (NLP, dialogue, representation learning), or multimodal AI, or on-device AI.
• Skills: Demonstrated teamwork and communication skills; proficiency in a neural network library such as PyTorch or TensorFlow; and a track record of research and publications in machine learning and artificial intelligence at conferences like NeurIPS, ICML, ICLR, AAAI, IJCAI, CVPR, ACL, EMNLP, NAACL, or TACL.