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About the Role

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.

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

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)
• Must be in-person in SF for the duration of the internship
• Must be authorized to work in the US (we support eVerify)
• Can start within the next month

Skills

• PyTorch
• HuggingFace

Location

• San Francisco, CA

Work Type

• On-site
• Internship

Experience Level

• Intern

Education Level

• MS or PhD preferred

About the Company

• 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.