Research Scientist ​/ Engineer – Training Infrastructure

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  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.

• 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 60-90 - Scale & Systemize:
  Build the monitoring and tooling that keeps large runs reliable and efficient.

• 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.

About Luma:
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. Luma is an equal opportunity employer.