Magic

We believe the most promising path to safe AGI is to automate AI research and code generation to improve models and solve alignment more reliably than humans can alone. Our approach is to combine frontier-scale pre-training, domain-specific reinforcement learning, ultra-long context, and inference-time compute to achieve this goal. To support our mission, we have thousands of GB200s and raised $768 million from Nat Friedman, Daniel Gross, CapitalG, Elad Gil, Sequoia, Jane Street, Eric Schmidt and others. We are a small group of engineers and researchers working to solve a short list of fundamental research problems on a direct path to AGI. If this sounds interesting, we would love to hear from you. Member of Technical Staff, Inference & RL SystemsSF Member of Technical Staff, KernelsSF / Remote Member of Technical Staff, Pre-training SystemsSF