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 Distributed Systems Engineer, you will build the data and coordination systems that enable ultra-long context inference and training on Magic’s GPU clusters.
What you might work on:
High-performance storage and caching systems to support long-context inference and training
Hacking on the internals of deep learning frameworks in a distributed setting
Automating fault detection and recovery systems to enable highly available training
Troubleshooting complex issues across GPUs, network, storage, OS, and cloud environments
What we’re looking for:
Deep knowledge of distributed systems design and public cloud platforms
Experience designing and operating highly available, high-throughput data systems
Experience with the internals of distributed DBMS, batch and stream processing systems, and/or distributed file systems
Exceptional problem-solving skills up and down the stack
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:
Integrity. Words and actions should be aligned
Hands-on. At Magic, everyone is building
Teamwork. We move as one team, not N individuals
Focus.Safely deploy AGI. Everything else is noise
Quality. Magic should feel like magic
Compensation, benefits and perks (US):
Annual salary range: $225K - $550K
Equity is a significant part of total compensation, in addition to salary
401(k) plan with 6% salary matching
Generous health, dental and vision insurance for you and your dependents
Unlimited paid time off
Visa sponsorship and relocation stipend to bring you to SF, if possible
A small, fast-paced, highly focused team