Research Engineer / Research Scientist, Health
$380k - $500k • San Francisco • FullTime
Posted 1mo ago
Job Location
San Francisco
Tech Stack
Remote Work Policy
On-site
Employment Type
FullTime
Categories
AI Research Engineer
About the job
OpenAI's Health team is dedicated to ensuring that AGI improves health for all humanity. This role focuses on building frontier health capabilities and scaling them into impactful products. You will tackle underdefined problems from inception to measurable improvement, working across the full model stack. We are seeking researchers with technical depth in areas like pretraining, reinforcement learning, or evaluations, or those with expertise in developing frontier biomedical AI capabilities. Prior healthcare experience is a plus, but research excellence, velocity, ownership, and mission alignment are paramount. This is a hands-on research position focused on driving real-world health outcomes through AI.
Responsibilities
- Own research directions end-to-end, from problem identification and measurement to experiment design and integration into frontier models.
- Develop scalable methods for pretraining, midtraining, reinforcement learning, and post-training to enhance health reasoning, knowledge, reliability, calibration, and behavior.
- Build and study RL environments grounded in real health problems to ensure robust and generalizable improvements.
- Create meaningful, trustworthy, and unsaturated evaluations to assess model improvements for patients and clinicians.
- Advance new 0→1 capabilities in health that can leverage increasing model intelligence and compute.
- Develop models and agents that can process longitudinal, multimodal health data for predictions, scientific insights, and decision support.
- Collaborate with researchers, engineers, clinicians, and product teams to integrate health capabilities into widely used models and products.
- Establish that AI improves real health outcomes, not just impressive answers or demos.
Requirements
- Strong track record in machine learning or AI research, with depth in pretraining, reinforcement learning/post-training, or evaluations.
- Substantial depth in a relevant frontier health/biomedical AI problem.
- Hands-on builder capable of designing experiments, writing and debugging code, and working with data and training infrastructure.
- Strong research taste to identify important problems and promising approaches.
- Ability to own ambiguous problems end-to-end and learn necessary skills.
- Goal-oriented approach, prioritizing high-leverage paths to impact.
- Focus on scalability, generalization, and real-world application of capabilities.
- Experience with health AI, longitudinal health data, or clinical research is a bonus.
Benefits
- Hybrid work model (3 days in office per week)
- Relocation assistance for new employees