Research Intern (Summer 2026)

Title: Research Intern (Summer 2026)

Location: San Francisco, CA, US / Remote (US)

## Overview

Cua 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.

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## 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

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## 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

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## 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

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## How to Apply

Please include:

• Your **CV** and GitHub/portfolio
• A short note on a research problem you’d like to tackle
• Bonus: try building something with [Cua]( or suggest a benchmark idea — we notice contributors

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**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.**