Member of Technical Staff, Pre-training Systems
About the role
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.
Responsibilities
- 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
- 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
Requirements
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
Qualifications
Master's degree in Computer Science, Electrical Engineering, or related field
At least 5 years of relevant work experience
Skills
Strong programming skills in Python, C++, or other relevant languages
Experience with distributed systems and cloud platforms (e.g., Kubernetes, AWS, GCP)
Knowledge of deep learning frameworks (e.g., PyTorch, TensorFlow)
Benefits
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
Pay
Annual salary range: $225K - $550K
Schedule
Full-time