Research Engineer / Research Scientist, Health

The posting, as OpenAI wrote it

About the Team

The Health team, within OpenAI’s broader Post Training organization, has a mission to ensure AGI improves health for all humanity.

Improving human health will be one of the defining impacts of AGI. Hundreds of millions of people already turn to ChatGPT for questions about their health and millions of clinicians use it weekly to support care delivery. Increasingly capable models create an opportunity to make high-quality medical intelligence more accessible across patients and clinicians—raising the floor of human health—and accelerate the new capabilities and scientific advances that raise the ceiling of human health.

Our job is to make those benefits real. We work across the full model stack—pretraining, midtraining, reinforcement learning, post-training, evaluations, harnessing, and deployment—and connect that research to the patients, clinicians, and real-world outcomes we aim to improve.

About the Role

We’re looking for an exceptional, hands-on researcher who wants to build frontier health capabilities and turn them into impact at scale. This is a role for someone who can take an important, underdefined problem from 0→1: identify the right bet, build what’s needed to test it, and drive it all the way to a measurable improvement in the models and products we actually ship.

We’re especially excited about two kinds of people: researchers with the technical depth to move the frontier in pretraining, reinforcement learning (RL) / post-training, or evals; and researchers with real depth in developing frontier biomedical AI capabilities. Prior experience in healthcare is helpful but not required. Research excellence, velocity, ownership, and alignment with the mission are most important to us.

This role is based in San Francisco, CA. We use a hybrid work model of 3 days in the office per week and offer relocation assistance to new employees.

In this role, you will

• Own a high-leverage research direction end to end—from deciding which problem matters and how to measure it, to designing and running experiments, to integrating successful work into frontier models.
• Develop scalable methods across pretraining, midtraining, reinforcement learning, and post-training to improve health reasoning, knowledge, reliability, calibration, and behavior.
• Build and study RL environments grounded in real health problems; understand which data and training setups produce robust, generalizable improvements rather than just higher benchmark scores.
• Create meaningful, trustworthy, and unsaturated evaluations that tell us whether our models are actually getting better at what matters for patients and clinicians.
• Advance new 0→1 capabilities in health that may have an increasing impact with upcoming increases in model intelligence and test-time compute.
• Develop models and agents that can work over longitudinal, multimodal health data to produce useful predictions, surface new scientific insights, and support better individ

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