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GeezerVison is showing magic.dev — Magic

is building frontier code models to automate software engineering and research.

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 pretraining, 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.

  • Research Engineer

    SF
  • Member of Technical Staff, Evals

    SF / Remote
  • Member of Technical Staff, Inference & RL Systems

    SF
  • Member of Technical Staff, Kernels

    SF / Remote
  • Member of Technical Staff, Pretraining Systems

    SF
  • Member of Technical Staff, RL Research & Environments

    SF
  • Member of Technical Staff, Security Engineer

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  • <insert-job-you-excel-at/>

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The latest

>10x More Efficient Pretraining

Research update on compute-efficient pretraining and scaling to trillion-parameter models.

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