Description
The Human Flourishing team’s mission is to ensure AI systems understand and advance human flourishing. As AI becomes more capable and plays a larger role in people’s lives, we want its human benefits to grow with its capabilities.
We’re looking for an exceptional, hands-on researcher to turn difficult-to-define signals of long-term flourishing into measurable improvements in model behavior. You’ll help set research direction, build what is needed to test your ideas, and drive successful work into the models and products we ship.
Responsibilities:
- Develop scalable training methods across the training stack that improve how models understand people’s values, support their agency, and help them achieve long-term goals.
- Lead high-leverage research from an open question to a shipped improvement: choose a hard-to-define problem, iteratively define success, run experiments, and integrate into frontier models.
- Build RL environments for difficult-to-verify outcomes, and study whether beneficial behavior generalizes across settings.
- Create evaluations grounded in human expertise and feedback that reveal capability gaps, unintended incentives in RL, and failures that aggregate scores can miss.
- Test whether measures of model behavior predict better learning, wellbeing, and other long-term outcomes, and use those findings to guide training.
- Build new 0→1 capabilities in human domains that may have an increasing impact with upcoming increases in model intelligence and test-time compute.
- Partner with product teams, engineers, partnerships teams, and domain experts to deploy promising methods, measure their effects in real use, and turn what you learn into further improvements.
- Assess AI’s impacts on human flourishing at the scale of over a billion users.
Requirements:
- Care deeply about helping AI improve people’s lives, and feel this is a responsibility that comes with increasingly capable models.
- Have an unusually strong research record in pretraining or RL / post-training research, especially in a hard-to-verify area; or combine strong LLM research skills with deep expertise in education, mental health, or a related field of human benefit.
- Are a hands-on builder who can turn ideas into rigorous experiments, write and debug code, and work directly with data and training infrastructure.
- Have the taste to choose consequential problems and actionable steps on the path to solving those problems.
- Take ownership of ambiguous problems and choose methods based on the impact they can deliver, including when the most useful work is unglamorous.
- 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.
- Work well across disciplines and can grok the most important bottlenecks to scaling our impact in a multidisciplinary problem.