Applied AI Intern

Kinetic Systems • San Francisco, CA

4d • Onsite

About The Position

Kinetic Systems is an early-stage startup working at the intersection of computer-use agents, human data, and healthcare. Our mission is to advance the capabilities of frontier AI models on economically meaningful healthcare tasks by building novel datasets, environments, and models. We were founded in 2025 out of the Stanford PhD program and backed by Tier 1 VCs. As an Applied AI Intern, you’ll help drive our research agenda towards advancing AI model capabilities for real-world healthcare tasks. You'll build novel evals and benchmarks to identify capability gaps in current models, publish academic papers, and post-train models to push the SOTA while staying grounded to the demonstrated needs of our healthcare partners.

Requirements

• Published at 1+ first-author papers in a top ML conference (NeurIPS, ICLR, ICML, etc.)
• Trained a 1B+ param model from scratch
• Come from a research background (preference for MS or PhD) in at least one of these fields: Computer-Use Agents, Vision-Language Models, Computer Vision, Robotics, RL
• Have significant experience with PyTorch, HuggingFace, or similar libraries
• Are high-agency and comfortable owning large, ambiguous problem spaces
• Are comfortable working long hours in a high-intensity, early-stage environment
• Are excited to be on-site in SF and collaborate closely with a small team
• Are interested in healthcare as an application (prior background not necessary)

Responsibilities

• Develop novel evals, RL environments, and benchmarks to reflect real-world healthcare workflows
• Develop, train, and evaluate computer-use agents for complex healthcare interfaces
• Build and maintain data pipelines to transform raw human data into high-quality training and evaluation assets
• Write and publish papers in academic conferences
• Work across the stack: models, tooling, infra, product, and internal workflows

What This Job Offers

• Career Level: Intern