Luma AI · 1mo ago
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.
You'll build the systems that make reinforcement learning work at frontier scale — coupling policy optimization with large fleets of inference workers, agentic environments, and the reward and verification systems that turn model behavior into learning signal.
You'll make Luma's multimodal models fast — profiling and optimizing GPU, CPU, and accelerator code so they train efficiently and deploy at scale without sacrificing quality. You'll write the kernels and operations that get the most out of the hardware.
As a Data Infrastructure Engineer in Research at Luma, you will play a critical role in building and scaling the data infrastructure that supports our cutting-edge multimodal AI systems.
You'll own how Luma's models get served — integrating new architectures into the inference engine, scaling deployments across thousands of machines, and keeping expensive GPU fleets busy while meeting internal SLOs.
You'll be Luma's creative lead on the ground with major brands and agencies, turning what our models can do into production workflows they actually use.
FDC Delivery leads Luma’ forward-deployed creative team operations across the region.
You will own how Dream Lab and Luma show up on the screens people actually watch. The behind-the-scenes reel from a shoot. The tutorial that makes a hard creative workflow look obvious.
You'll be Luma's creative lead on the ground with major brands and agencies, turning what our models can do into production workflows they actually use.
Redwood City, USA
Canada
Munich, Germany