PhD Machine Learning Intern (Summer 2026)

Remote - US: All locationsFull-TimeInternAI / Data Science

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This internship offers the opportunity to translate your research into real-world impact while gaining experience with large-scale production systems and cross-functional collaboration in a fast-moving, high-impact environment.

Research and prototype innovative machine learning approaches in areas such as Search, Large Language Models (LLMs), Multimodal Content Understanding, and Recommender Systems
Design and implement end-to-end ML pipelines—from data exploration to model training, evaluation, and deployment—in collaboration with mentors and product teams
Analyze large-scale datasets to identify opportunities for personalization and improved user experiences
Partner with Product, Design, and Engineering teams to integrate models into Dropbox products
Contribute to the team’s technical discussions, offering research-based perspectives to guide experimentation and long-term strategy

Currently pursuing a PhD in Computer Science, Machine Learning, Artificial Intelligence, or a closely related field, with a research focus on advanced AI
Graduation date in Winter 2027 or Spring/Summer 2028.
Proven experience in applied AI, through research publications, significant projects, or internships where you built or tested advanced ML solutions.
Strong coding skills (e.g., Python, PyTorch, scikit-learn, or similar ML libraries), plus an ability to quickly prototype and iterate on cutting-edge ideas.
Curiosity and drive to explore novel ML methodologies and translate them into practical applications that solve user needs.
Research experience in one or more of the following: Natural Language Processing, Large Language Models, Deep Learning, Recommender Systems, Learning to Rank, Speech Processing, Graph Learning
Strong analytical and problem-solving skills, with the ability to bridge research and practical application
Excellent communication and collaboration skills, especially in interdisciplinary teams
Familiarity with modern ML infrastructure and large-scale data systems

Publications in top-tier ML, AI, or NLP conferences (e.g., NeurIPS, ICLR, ICML, ACL, EMNLP, KDD, SIGIR, CVPR)
Experience with LLM-based applications or Retrieval-Augmented Generation (RAG) systems
Interest in translating research insights into product impact for real-world users
Familiarity with the end-to-end process of building AI prototypes: from data exploration and model iteration to evaluation and user testing.
Passion for swiftly moving from idea to experiment, comfortable with ambiguous or evolving project goals.