PhD in mathematics, physics, or a closely related quantitative field. Your work in AI and machine learning is driven by curiosity, and you’ve brought a depth of mathematical thinking that clearly distinguishes your work.
You have spent meaningful time on frontier research and you already have an understanding of how top research teams operate, from problem selection to large-scale experimentation to shipping results that matter.
Your research interests might span generative models, geometric deep learning, probabilistic modeling, dynamical systems, information geometry, optimization theory, or related areas. What matters most is that you think from first principles and bring mathematical structure to hard problems.
You have a strong publication record at top ML venues (NeurIPS, ICML, ICLR, or equivalent) that demonstrates independent thinking and a coherent research arc.
You are genuinely curious about biology. Domain expertise is welcome, and what we really care about is that you find it exciting to work with data from unexplored ecosystems encoding billions of years of evolutionary optimization. The best candidates will see biological data as a source of fundamentally new scientific questions.
You have low ego, strong collaborative instincts, and a startup mentality. You are comfortable with ambiguity, willing to iterate fast, and happy to work in a small team where everyone contributes across boundaries.