OpenAI2 days ago

Research Engineer / Research Scientist, Health

Salary not specified

MARKET

15,900 ₽median for this role

Data Scientist · 115 jobs with disclosed pay

5,000half of the offers: 12,796–20,50043,793

The employer didn't disclose pay — compare with the market yourself.

Полная занятостьSan Francisco

Responsibilities

01Own 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
02Develop scalable methods across pretraining, midtraining, reinforcement learning, and post-training to improve health reasoning, knowledge, reliability, calibration, and behavior
03Build 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
04Create meaningful, trustworthy, and unsaturated evaluations that tell us whether our models are actually getting better at what matters for patients and clinicians
05Advance new 0→1 capabilities in health that may have an increasing impact with upcoming increases in model intelligence and test-time compute
06Develop models and agents that can work over longitudinal, multimodal health data to produce useful predictions, surface new scientific insights, and support better individual- and population-level decisions
07Work closely with researchers, engineers, clinicians, and product teams to bring health capabilities into the models and products used by hundreds of millions of people
08Help establish that AI improves real health outcomes, not just that it can produce impressive answers or demos

01Care deeply about using increasingly capable AI to improve human health, and take seriously the responsibility of deploying it safely
02Have an unusually strong track record in machine learning or AI research, with the depth to push at least one important area forward: pretraining, reinforcement learning / post-training, evals (not necessarily specific to health); or substantial depth in a relevant frontier health / biomedical AI problem
03Are a hands-on builder: you can design experiments, write and debug code, work directly with data and training infrastructure, and move quickly from an idea to evidence
04Have strong research taste and can distinguish an important problem or a promising approach from an interesting one
05Own ambiguous problems end to end, and are willing to learn whatever is missing to get the job done
06Stay goal-oriented rather than method-oriented, and don't shy away from unglamorous work if it is the highest-leverage path to impact
07Care about whether a capability will scale, generalize, and make it into the real world—not just whether it can be shown in a paper or prototype
08Enjoy working with low-ego, ambitious people across research, engineering, medicine, and product
09Bonus: Experience with health AI, longitudinal health data, or clinical research