Luma AI
Full TimeBuild distributed training systems that scale Luma's multimodal models across thousands of GPUs using PyTorch, CUDA, and advanced parallelism techniques like FSDP and tensor parallelism. You'll design and optimize infrastructure for training stability, convergence, and resource utilization, plus develop monitoring and debugging tools for massive clusters. This requires deep experience training foundation models at multi-node scale, GPU cluster architecture, and communication libraries like NCCL. The role is based in San Francisco and compensated competitively, though specific salary is not listed.
Written from this posting by Neural Jobs AI. The full description is below.
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.
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.
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Luma AI builds generative models for 3D capture and video generation, including the Dream Machine video model.
Founded in 2021, Luma focuses on multimodal models that understand and generate the physical world.
United States Hybrid
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