
Magic is working on frontier-scale code models to build a coworker, not just a copilot. Come join us: http://magic.dev

Magic is working on frontier-scale code models to build a coworker, not just a copilot. Come join us: http://magic.dev
Headquarters: San Francisco, CA
Focus: Frontier-scale generative AI models for code and research automation
Founding year: 2022
Founders: Eric Steinberger; Sebastian De Ro
Total funding (reported): Approximately $465M–$515M
Employee count (snapshot): 99
Automating software engineering and AI research using frontier-scale language models.
2022
Artificial intelligence; developer tools
$320,000,000
Reported contributions from Eric Schmidt, CapitalG (Alphabet), Sequoia, Atlassian, Jane Street, and individual investors including Nat Friedman, Daniel Gross and Elad Gil.
$23,000,000
Participation from Elad Gil, Nat Friedman and Amplify Partners.
“Includes strategic and high-profile investors (Eric Schmidt, CapitalG/Alphabet, Sequoia, Atlassian, Jane Street) and notable individual investors (Nat Friedman, Daniel Gross, Elad Gil).”
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Magic’s mission is to build safe AGI that accelerates humanity’s progress on the world’s most important problems. We believe the most promising path to safe AGI lies in automating research and code generation to improve models and solve alignment more reliably than humans can alone. Our approach combines frontier-scale pre-training, domain-specific RL, ultra-long context, and inference-time compute to achieve this goal.
About The Role As a Software Engineer on the Pre-training Systems team, you will design and operate the distributed infrastructure that trains Magic’s long-context models at scale.
This role focuses on large-scale model training across massive GPU clusters. You will work at the boundary between deep learning and distributed systems, ensuring that training runs are performant, reliable, and reproducible under extreme scale.
Magic’s long-context models create non-trivial systems challenges: sustained memory pressure, communication overhead across thousands of devices, long-running jobs that must survive failures, and efficient sequence packing under hardware constraints. You will own the systems that make large-scale pre-training stable and fast.
What you’ll work on
What we’re looking for
Compensation, benefits, and perks (US):
Magic strives to be the place where high-potential individuals can do their best work. We value quick learning and grit just as much as skill and experience.
Our culture
Compensation Range: $225K - $550K
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