Research Intern (Summer 2026)

About Cua

Give every agent a cloud desktop

Tech

We're looking for different roles to help us push this vision forward - turning cutting-edge research prototypes into real, deployable systems. If you’re obsessed with developer tools, infrastructure, and making AI agents go from toy demos to robust, real-world tools - we want to talk.

The role

## OverviewCua is building the infrastructure that enables general-purpose AI agents to safely and scalably use real computers and applications.We're a small team backed by **Y Combinator** and top-tier investors, and our open-source tools are already used by thousands of developers. As a **Research Intern**, you’ll help prototype, test, and benchmark multi-modal LLM-based agents - from data pipelines to orchestration systems.You’ll collaborate with engineers and researchers to turn cutting-edge ideas into real systems and benchmarks that can be shared with the community. This is a chance to contribute to open-source research, design experiments, and explore the frontiers of agentic AI.---## Responsibilities* Generate and curate large-scale, high-quality multi-modal data (GUIs, browsers, system UIs)* Design and test single- and multi-agent systems for data and computer use* Automate benchmarking of agent orchestration (with or without human-in-the-loop)* Explore new training and inference techniques to boost reasoning and action-taking (e.g., RL-based agents)* Develop benchmarks, tools, and datasets to evaluate agentic capabilities on Cua* Collaborate with the founding team and contribute to research publications, open-source tools, and the broader community---## Qualifications### Required:* Currently a **PhD student** in Computer Science or related field (strong Master’s considered)* Experience in applied research with a solid publication record* Familiarity with modern multi-modal or reasoning agents (e.g., OS-Atlas, Qwen, GUI-R1)* Hands-on experience with **PyTorch**, **Python**, and cloud compute (AWS, GCP, etc.)* Comfortable designing experiments, evaluating models, and working with multi-modal data* Excited by generative AI, agent systems, and pushing the boundaries of what’s possible### Preferred:* Experience with reinforcement learning or agent-based training methods* Prior contributions to open-source projects or benchmark design* Familiarity with large-scale dataset construction and evaluation pipelines* Interest in bridging research and engineering for real-world applications* Based in or able to spend time in **SF/Bay Area** (preferred), but remote OK---## What We Offer* **Research impact** – Opportunity to publish, open-source, and influence open agent research* **Hands-on projects** – Work directly with engineers and researchers on cutting-edge systems* **Open-source visibility** – Contribute benchmarks and datasets used by the community* **Flexible setup** – Remote-friendly; SF-based team* **Learning environment** – Collaborate on projects at the intersection of infrastructure and AI research---## How to ApplyPlease include:* Your **CV** and GitHub/portfolio* A short note on a research problem you’d like to tackle* Bonus: try building something with [Cua](https://github.com/trycua/cua) or suggest a benchmark idea — we notice contributors---**This is a paid internship (3-month full-time preferred; part-time considered). Compensation will depend on location and experience.****Cua AI, Inc. is committed to fair and transparent opportunities. We encourage applicants from all backgrounds, identities, and walks of life to apply.****Personal data will be handled in accordance with the GDPR (EU Regulation 2016/679) and other applicable data privacy laws.**