
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 Data team, you will design and operate the systems that define our model’s training corpus at scale.
This role is focused on large-scale data acquisition, processing, filtering, mixture design, and ablation-driven iteration. You will work on the infrastructure and experimental loops that determine what data we train on — and therefore what the model learns.
Magic’s long-context models introduce non-trivial data challenges: maintaining document structure and long-range coherence, designing sequence chunking and packing strategies, balancing mixture trade-offs, and ensuring data quality at internet scale. You will own systems that turn these questions into measurable training decisions.
This role can evolve into broader ownership of corpus strategy, deeper involvement in training systems, or transition into ML systems work as you shape how data and model behavior interact at scale.
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: $200K - $550K
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