Kinetic Systems is an applied research lab building data infrastructure for autonomous healthcare. 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 (Research Scientist), you’ll help drive our research agenda towards advancing AI model capabilities for real-world healthcare tasks. You'll be joining an energetic, early-stage startup environment and have ownership across the full stack of what we do: product, research, training, evals, and infrastructure.
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
Develop, train, and evaluate computer-use agents for complex healthcare interfaces
Write and publish papers in academic conferences
Masters or PhD in relevant field (CS, ML, biomedical data science)
Prior work experience at a frontier lab, top academic research lab, or RLE/human data company
Published 1+ first-author papers in a top ML conference (NeurIPS, ICLR, ICML, etc.)
Strong experience with PyTorch, HuggingFace, or similar libraries
Have built and delivered RLEs, benchmarks, or evals to frontier lab customers
Have direct experience post-training LLMs
Can independently brainstorm, design, and execute rigorous research projects
Prior background in healthcare AI research is a big plus (but not required)
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
Kinetic Systems is a Stanford PhD spinout whose mission is to advance the capabilities of frontier AI models for solving clinically and economically meaningful healthcare tasks. We were founded in 2025 and are backed by General Catalyst.
As an Data Operations Lead, you will interface with AI and healthcare customers, scope requirements, and help construct high quality datasets, environments, and evaluations for AI models.
Lead strategies for generating / acquiring novel datasets and partnerships
Manage expert contributors
Collaborate with researchers and engineers to align data with overall research agendas
Develop and implement processes for curating and QC'ing data pipelines at scale
Previous work experience building expert networks or delivering human data projects
Are scrappy, hands-on, and willing to do whatever it takes to deliver
Understand how to improve, incentivize, and maintain contributor networks
Have had at least one job in healthcare and moderate domain knowledge
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
If you join our team, you will be joining a team with...
Founding-level impact and ownership
Leading AI research + healthcare expertise
Competitive compensation and meaningful equity
An opportunity to advance AI research in one of its most meaningful application areas
You'll also receive...
Unlimited PTO
Comprehensive health, dental, and vision coverage
401(k)
Free lunch + dinner
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
Kinetic Systems is a Stanford PhD spinout whose mission is to advance the capabilities of frontier AI models for solving clinically and economically meaningful healthcare tasks. We were founded in 2025 and are backed by General Catalyst.
As a Healthcare GTM Lead, you will help scale Kinetic’s healthcare partnerships and GTM function. You will identify, negotiate, close, and manage partnerships with health systems, medical groups, specialty practices, data networks, and other organizations.
Handle the end-to-end sales cycle with healthcare organizations and own a large chunk of the partner pipeline, including: outbound motion, pitch materials, CRM process, diligence checklist, and success metrics.
Work closely with researchers and engineers to align with overall engineering and research agendas
You have 5+ years of experience in healthcare partnerships, business development, data licensing, RWD, health tech, life sciences data, provider strategy, or enterprise healthcare sales.
You understand healthcare data: EHR data, claims, notes, imaging, labs, registries, FHIR/HL7, de-identification, tokenization, data quality, governance, and the practical messiness of getting data out of clinical systems.
You can credibly discuss HIPAA, de-identification, DUAs, BAAs, IRB/research review, data governance, information security review, and commercial data licensing structures.
You have strong executive presence and can build trust with health system C-suites, general counsel, privacy officers, clinical informatics leaders, research leaders, and data governance committees.
You are scrappy, high-agency, and willing to do unglamorous work: cold outbound, conference follow-up, contract redlines, data dictionaries, partner Q&A, procurement portals, and last-mile operational details.
Are comfortable working long hours in a high-intensity, early-stage environment
On-site or remote is equally acceptable
If you join our team, you will be joining a team with...
Founding-level impact and ownership
Leading AI research + healthcare expertise
Competitive compensation and meaningful equity
An opportunity to advance AI research in one of its most meaningful application areas
You'll also receive...
Unlimited PTO
Comprehensive health, dental, and vision coverage
401(k)
Free lunch + dinner
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 an Intern, you’ll get hands-on experience across the full stack of what we do: product, research, training, evals, and infrastructure.
Note: This is a full-time role, required to be in-person in SF. Internships must be at least 3-months.
Whatever it takes! Please read the other job descriptions to get a sense of what we do
Have experience with PyTorch, HuggingFace, or similar libraries
Familiar with best practices around RLEs, benchmarks, evals, and post-training
Interested in healthcare as an application (prior background not necessary)
If you join our team, you will be joining a team with leading healthcare + AI expertise, and an opportunity to help advance AI research in one of its most meaningful application areas.
","descriptionPlain":"Kinetic Systems is an applied research lab building data infrastructure for the autonomous hospital.\n\nWe 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.\n\n \n\n\nTHE ROLE\n\nAs an Intern, you’ll get hands-on experience across the full stack of what we do: product, research, training, evals, and infrastructure.\n\nNote: This is a full-time role, required to be in-person in SF. Internships must be at least 3-months.\n\n \n\n\nWHAT YOU’LL DO\n\n - Whatever it takes! Please read the other job descriptions to get a sense of what we do\n\n \n\n\nWHO YOU ARE\n\n - Have experience with PyTorch, HuggingFace, or similar libraries\n\n - Familiar with best practices around RLEs, benchmarks, evals, and post-training\n\n - Interested in healthcare as an application (prior background not necessary)\n\n \n\n\nWHY US\n\nIf you join our team, you will be joining a team with leading healthcare + AI expertise, and an opportunity to help advance AI research in one of its most meaningful application areas."},{"id":"71c2ac8f-ea9c-4d60-a18a-350a637e34b9","title":"Orthopedic Surgeon, Clinical Expert (Contract)","department":"Clinical","team":"Clinical","employmentType":"Contract","location":"San Francisco","secondaryLocations":[],"publishedAt":"2026-10-28T15:50:02.641+00:00","isListed":true,"isRemote":false,"workplaceType":"OnSite","address":{"postalAddress":{"addressRegion":"California","addressCountry":"United States","addressLocality":"San Francisco"}},"jobUrl":"https://jobs.ashbyhq.com/kineticsystems/71c2ac8f-ea9c-4d60-a18a-350a637e34b9","applyUrl":"https://jobs.ashbyhq.com/kineticsystems/71c2ac8f-ea9c-4d60-a18a-350a637e34b9/application","descriptionHtml":"Orthopedic Surgeon, Clinical Expert (Contract)
San Francisco
10–40 hours/week for 1 month
$200 - $400/hr
About the role
You will work with an applied AI research lab at the intersection of healthcare and AI. We're looking for board-certified orthopedic surgeons to bring real musculoskeletal and surgical judgment into how AI is tested and improved.
This is a part-time contract role that fits alongside clinical practice. No coding or AI experience is required; we'll teach you the tools. What matters most is your clinical eye: knowing which finding on exam or imaging should change the treatment plan.
What you'll do
Clinical judgment is hard to teach, and that's where you come in. You'll partner with our researchers to show them what good care looks like in your specialty, from the paperwork and handoffs to the decisions that never make it into a textbook.
You'll then put that expertise into practice by authoring clinical cases from the ground up. For each one, you'll assemble a realistic patient chart, pose the clinical question, answer it yourself, and decide what separates a strong response from a weak or unsafe one.
Requirements
MD or DO with an active, unrestricted medical license in any US state
Board certified in Orthopedic Surgery (ABOS or AOBOS), or board eligible
Completed an accredited orthopedic surgery residency in the US
Clinical practice within the last 3 years
Availability to work on site in San Francisco, 10 to 40 hours per week for 1 month
Nice to have
Fellowship training (sports, adult reconstruction, spine, hand and upper extremity, trauma, foot and ankle, or shoulder and elbow)
Experience in academic, private practice, ASC, or hospital-employed settings
Command of orthopedic coding, prior authorization, and AAOS appropriate use criteria
Writing board-style questions, teaching cases, or CME or residency faculty experience
Utilization review, peer review, QI, or IME work
Any past work reviewing AI outputs or writing rubrics