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.
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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
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PhD candidate in Computer Science or a related field graduating between Winter 2026 and Spring 2028.
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Proven experience in systems/infrastructure and/or AI/ML through research publications, significant projects, or internships where you built or tested advanced solutions.
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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.
Preferred Qualifications
- Prior systems publications, open-source contributions, or experience operating large-scale infrastructure
- Interest in translating research insights into product impact for real-world users
- Familiarity and passion for swiftly moving from idea to experiment, comfortable with ambiguous or evolving project goals
Compensation
