6 months ago
Base Salary
$225k - $550k/yr
Responsibilities
- Scale distributed training across large GPU clusters using data, tensor, and 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 machine learning systems.
- Strong ownership mindset and ability to operate critical infrastructure.
- Track record of improving performance or reliability of large-scale systems.
Benefits
- Annual salary range of $225K-$550K plus significant equity compensation.
- 401(k) plan with 6% salary matching.
- Health, dental, and vision insurance for employees and dependents.
- Unlimited paid time off.
- Visa sponsorship and relocation stipend to San Francisco, if possible.
- Small, fast-paced, highly focused team environment.
Categories
About Magic
Magic is working on frontier-scale code models to build a coworker, not just a copilot. Come join us: http://magic.dev