@ Anthrogen
Anthrogen — Member of Technical Staff, Research Scientist (2 openings). Location: San Francisco, in person. Comp: $250k–$500k depending on experience, 0.15–0.6% equity, ~1.5x industry average. Experience: Indexing primarily on publications and frontier-lab experience; hard credentials matter less (though the strongest research scientists often come out of top academic PhD programs). Exceptional new grads with standout research records welcome. Target labs/orgs: frontier labs (SSI, Thinking Machines), Baker lab, Tarokh lab, Prorok lab (Cambridge), and top academic ML groups.
Role summary: We want researchers who would be an exceptional hire at any frontier AI lab — irrespective of bio background. They'll drive original research on the architectures, training objectives, and scaling behind our models, owning an agenda from idea to result. We're a small, high-density team building frontier AI and the biological systems to validate it; we index on research taste and output, not credentials.
Critical (must-haves): Would be a strong hire at any frontier AI lab — track record of original research (first-author work at NeurIPS/ICML/ICLR or equivalent frontier-lab work / impactful open models). Deep command of modern deep learning: generative models (diffusion, autoregressive), architectures, optimization, and scaling. Drives a research agenda independently, from idea → experiment → result. High-agency and in-person, comfortable with the ambiguity of frontier research.
Important (bonuses): Frontier-lab experience (SSI, Thinking Machines, or equivalent), a top industrial research group, or pedigree from ai/bio labs like Baker (UW), Ovchinnikov (MIT), many Stanford or Cambridge/Oxford labs. PhD from a top academic ML program (or comparable research maturity without one — even a Bachelors is fine with the requisite research maturity). Range across subfields — a strong researcher who can pick up a new domain fast, or has spent meaningful time in an ML-for-something domain (robotics, biology, physics).
Nice-to-haves: Interest in biology / protein design / AI for science. Widely-used open-source models or research artifacts. Familiarity with the protein-ML landscape (ESM, AlphaFold, RFdiffusion).