Research Scientist / Engineer – Training Infrastructure

🔥 12 hours ago

🇪🇺 Europe – Remote

⏰ Full Time

🟡 Mid-level

🟠 Senior

🧬 Research Scientist

👻 Ghost score 12%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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Luma AI

WebsiteLinkedInAll Job Openings

2 - 10 employees

🤖 Artificial Intelligence

☁️ SaaS

📱 Media

💰 $90M Series B on 2025-01

Artificial Intelligence • SaaS • Media

Luma AI is a Palo Alto–based company building multimodal generative AI models and creative workflow tools focused on video, image, audio, and text production. Luma offers models and products such as Ray (video generation), UNI-1/Uni-1. 1 (brand intelligence models), and an API/enterprise offerings that allow creative teams to generate, edit, and direct cinematic-quality assets with brand continuity. They emphasize frontier research (Open Physical AI Lab), in-house model development, and products for creative teams, agencies, and production services. Luma also provides APIs, enterprise plans, a creative partner program, and learning resources.

📋 Description

• Design, implement, and optimize efficient distributed training systems for models across thousands of GPUs • Research and implement advanced parallelization, including FSDP, Tensor Parallel, Pipeline Parallel, and Expert Parallel • Build monitoring, visualization, and debugging tools for large-scale training runs • Optimize training stability, convergence, and resource utilization across massive clusters • Learn the current training stack and diagnose stability and utilization issues at scale • Deliver a parallelization or stability improvement that measurably benefits a real training run • Build monitoring and tooling to keep large runs reliable and efficient

🎯 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 (nice to have) • Experience managing training runs across 100+ GPUs (nice to have) • Experience with containerization, orchestration, and cloud infrastructure (nice to have) • Experience at the level of FSDP and multi-node training • Ability to work remotely in the EU

🏖️ Benefits

• Equal opportunity employer • Voluntary diversity and inclusion survey participation; refusal will not affect the job application • Remote work arrangement