Luma AI is the creative AI platform for video generation and image creation. Powered by the world's leading video generation models, Ray and Uni, and creative agents handling end-to-end workflows. Trusted by leading agencies and brands. Try it free. Backed by General Catalyst, a16z and CRV.
About the role
You'll build the distributed systems that train Luma's large-scale multimodal models across thousands of GPUs, so researchers can focus on innovation on top of reliable, efficient, scalable infrastructure.
What they're looking for
- 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
More about this role
You'll build the distributed systems that train Luma's large-scale multimodal models across thousands of GPUs, so researchers can focus on innovation on top of reliable, efficient, scalable infrastructure.
This is hard PyTorch, CUDA, and distributed-systems work — advanced parallelism, training stability, and utilization across massive clusters. It fits an engineer who's solved real problems training foundation models at scale. If you haven't worked at the level of FSDP and multi-node training, this is the wrong depth.
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.
One way the first 90 could unfold.
Days 1–30 — Immerse & Diagnose: Learn the current training stack and where stability and utilization hurt at scale.
Days 30–60 — Ship & Validate: Land a parallelization or stability improvement that measurably helps a real training run.
Days...
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