Member of Technical Staff, Pretraining Systems

As a Research Engineer on the Pretraining 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 pretraining stable and fast.

• Scale distributed training across large GPU clusters (data, tensor, pipeline parallelism)
• Optimize communication patterns and gradient synchronization
• Improve checkpointing, fault tolerance, and job recovery systems