Research Scientist / Engineer – Training Infrastructure

What You'll Own

• 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.

First 90 Days

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

What You Bring

• 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.

Nice to Have

• 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.