Research Scientist / Engineer – Training Infrastructure

Luma is seeking a Research Scientist / Engineer – Training Infrastructure to join their team in Global Village, Dubai, United Arab Emirates. You will build the distributed systems that train Luma's large-scale multimodal models across thousands of GPUs, enabling researchers to focus on innovation on top of reliable, efficient, scalable infrastructure. Join us to shape the future of AI.

Looking for a Research Scientist / Engineer – Training Infrastructure at Luma in Global Village, Dubai, United Arab Emirates? This role focuses on the day-to-day work of a 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 parall

Company Overview

Luma AI’s mission is to build Multimodal AGI: AI that can generate, understand, and operate in the physical world. We develop multimodal models across video, 3D, and generative media, and ship them in products like Dream Machine to help creators and teams turn ideas into compelling visuals—fast. Luma is an equal opportunity employer.

Quick Details

• Salary Range: $195,000.00/yr - $395,000.00/yr
• Job Type: Full-time
• Qualifications: Masters Degree
• Experience: 5 Year+

Key Responsibilities

• 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.

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.
• Experience solving real problems training foundation models at scale.
• Experience with FSDP and multi-node training.

Benefits

• Opportunity to work on cutting-edge AI infrastructure.
• Contribute to building Multimodal AGI.
• Work with advanced parallelism techniques.
• Develop tools for large-scale training runs.
• Optimize training stability and resource utilization.