Research Engineer

OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our mission, we must encompass and value the many different perspectives, voices, and experiences that form the full spectrum of humanity.

• 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 individual- and population-level decisions.
• Work closely with researchers, engineers, clinicians, and product teams to bring health capabilities into the models and products used by hundreds of millions of people.
• Help establish that AI improves real health outcomes, not just that it can produce impressive answers or demos.

• Care deeply about using increasingly capable AI to improve human health, and take seriously the responsibility of deploying it safely.
• Have 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.
• Are 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.
• Have strong research taste and can distinguish an important problem or a promising approach from an interesting one.
• Own ambiguous problems end to end, and are willing to learn whatever is missing to get the job done.
• Stay goal-oriented rather than method-oriented, and don’t shy away from unglamorous work if it is the highest-leverage path to impact.
• Care 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.
• Enjoy working with low-ego, ambitious people across research, engineering, medicine, and product.
• Bonus: Experience with health AI, longitudinal health data, or clinical research.

• 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.