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. This is large-scale inference systems work: scheduling, fleet management, deployment

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. RL is how Luma's models go from capable

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. This is deep performance work: fused kernels,

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 stab

You'll be Luma's creative lead on the ground with major brands and agencies across France, turning what our models can do into production workflows they actually use. Sitting between the technology and the craft, you'll shape how top creators work with generative video, and produce high-craft work i