micro1 is a data lab that helps AI labs & Enterprises train foundational models and agents. We provide frontier evaluations and reinforcement learning environments to improve LLM capabilities, as well as contextual evaluations to monitor and improve AI agents in enterprise settings.

The role centers on identifying and framing foundational open problems, theoretical bounds, or simulation challenges where verifiable progress is possible, then guiding frontier AI models and agents through multi-step research, from proofs and algorithms to numerical experiments and high-performance simulations. You'll validate results through proof checks, computational reproduction, and formal methods, paying close attention to where model-generated reasoning breaks down, and judge the novelty, correctness, and significance of outputs against current literature. Attempts, partial solutions, and observed model failure patterns get written up in concise research memos. You'll also advise on research direction, recommending the subfields and open problems best suited to progress with AI models and computational tools.