Kinetic Systems is an applied research lab building data infrastructure for the autonomous hospital.
We spun out of the Stanford PhD program in 2025 and are backed by General Catalyst. Our mission is to advance the capabilities of frontier models for solving clinically and economically meaningful healthcare tasks.
As a Member of Technical Staff (Software Engineer), you’ll be closely involved in all aspects of engineering, with a particular eye on building RL environments, translating research into deliverables and product, building out core infrastructure and back-end systems, and developing user-facing websites and APIs. We move at the speed of research (i.e. very fast), and you will need to as well!
Note: This is a full-time role, required to be in-person in SF.
Develop novel evals, RL environments, and benchmarks to reflect real-world healthcare workflows
Build websites and other applications
Maintain customer-facing products
Manage a team of developers
Own internal tooling (data pipelines, dashboards, observability, etc.) that helps our research team move faster
Translate research code into robust, scalable software
2+ years industry SWE experience
Strong background in React, NextJS, Tailwind, and SQL
Proficient in DevOps / CloudOps (e.g. Docker, Terraform, etc.)
Familiar with RLEs, benchmarks, evals, and post-training
Experience working with sensitive data (health, finance, etc.)
Interested in healthcare as an application (prior background not necessary)
If you join our team, you will be joining a team with...
Founding-level impact and ownership
An opportunity to advance AI research in one of its most meaningful application areas
Competitive compensation and meaningful equity
Unlimited PTO
Comprehensive health, dental, and vision coverage
401(k)
Free lunch + dinner
Job details are sourced from the employer's original posting.
Open job postingAbout the company
Kinetic Systems is an applied research lab building data infrastructure for the autonomous hospital. They focus on advancing the capabilities of frontier models for solving clinically and economically meaningful healthcare tasks.