Role Description

Dropbox powers collaboration and cloud infrastructure for over 700 million registered users worldwide. We’re building the next generation of scalable storage, distributed compute, large-scale content processing, and secure infrastructure, with AI/ML workloads top-of-mind.

As a PhD Systems & AI/ML Research Intern, you’ll work on meaningful infrastructure problems at the intersection of distributed systems, backend services, and AI/ML workloads. Supported by senior engineers, you’ll own a project aligned with your research, that results in a working prototype with the goal of a submission for adoption internally and external publication. Come help us define and build the infra layer for our next-gen products.

Responsibilities

• Execute a defined engineering/research project aligned with your research.
• Implement a prototype that addresses a concrete infrastructure or AI-systems challenge.
• Build and run benchmarks using realistic workloads to measure performance, reliability, or security improvements.
• Document the design and implementation, including assumptions, architecture, tradeoffs, and evaluation results.
• Deliver a final artifact that includes a working prototype, reproducible results, technical write-up, and internal presentation.
• Collaborate with mentors to refine the approach, validate results, and, if appropriate, prepare findings for internal adoption or external publication.

Requirements

• PhD candidate in Computer Science or a related field graduating between Winter 2026 and Spring 2028.
• Proven experience in systems/infrastructure and/or AI/ML through research publications, significant projects, or internships where you built or tested advanced solutions.
• Strong coding skills (e.g., Python, Rust, Go, C++) plus an ability to quickly prototype and iterate on cutting-edge ideas.
• Curiosity and drive to explore novel systems methodologies and translate them into practical applications that solve practical needs.
• Excellent communication and collaboration skills, especially in interdisciplinary teams.