Member of Technical Staff, Pretraining Systems

The role

You will design and operate distributed infrastructure to train long-context models at scale. The role focuses on large-scale model training across massive GPU clusters.

What you will do

• Scale distributed training across large GPU clusters
• Optimize communication patterns and gradient synchronization
• Improve checkpointing, fault tolerance, and job recovery systems
• Profile and eliminate performance bottlenecks across compute, networking, and storage
• Improve experiment reproducibility and orchestration workflows
• Increase hardware utilization and training throughput
• Collaborate with Kernels and Research to align model architecture with systems realities

Required qualifications

• Strong software engineering and distributed systems fundamentals
• Experience training large models in multi-node GPU environments
• Deep understanding of parallelism strategies and performance trade-offs
• Experience debugging cross-layer issues in production ML systems
• Strong ownership mindset and ability to operate critical infrastructure
• Track record of improving performance or reliability of large-scale systems

Benefits

• 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

About the company

Magic's mission is to build safe AGI that accelerates humanity's progress on the world's most important problems. The company believes the most promising path to safe AGI lies in automating research and code generation.