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About the Role
Build the distributed systems that train Luma's large-scale multimodal models across thousands of GPUs, enabling researchers to focus on innovation on top of reliable, efficient, scalable infrastructure. This role involves advanced PyTorch, CUDA, and distributed-systems work, focusing on parallelism, training stability, and utilization across massive clusters.
Responsibilities
- Design, implement, and optimize efficient distributed training systems for models across thousands of GPUs.
- Research and implement advanced parallelization (FSDP, Tensor Parallel, Pipeline Parallel, Expert Parallel).
- Build monitoring, visualization, and debugging tools for large-scale training runs.
- Optimize training stability, convergence, and resource utilization across massive clusters.
Requirements
- Extensive distributed PyTorch training and parallelisms in foundation-model training.
- Deep understanding of GPU clusters, networking, and storage systems.
- Familiarity with communication libraries (NCCL, MPI) and distributed-system optimization.
- Strong Linux systems administration and scripting.
- Experience managing training runs across 100+ GPUs.
- Experience with containerization, orchestration, and cloud infrastructure.
Skills
- PyTorch
- CUDA
- Distributed Systems
- FSDP
- NCCL
- MPI
- Linux systems administration
- Scripting
- Containerization
- Orchestration
- Cloud infrastructure
Location
- Remote
Work Type
- Full-time
Experience Level
- Senior
- Foundation Model Training at Scale
About the Company
- Luma's mission is to build unified general intelligence that can generate, understand, and operate in the physical world.
- We believe multimodality is critical for intelligence — the next step beyond language models comes from vision.
Equal Opportunity
- Luma is an equal opportunity employer.