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At Luma, the Realtime Interactive team is responsible for building an entirely new paradigm for generated content, allowing users to interact with a model’s outputs as they are being generated. The team works closely with the Fundamental Research, Data, and Performance Optimization teams to train fast and efficient realtime models as World Simulators and beyond.
Job Responsibility:
Work on top of pretrained multimodal generative models to fine-tune and optimize them for realtime generation
Design novel algorithms and techniques to solve problems with autoregressive visual generation, long-range temporal consistency, and long-term memory
Develop interactive applications with tight latency constraints
Process data to develop advanced interactive capabilities and controls for World Modeling, such as controlling character and camera movement, audio, and more
Requirements:
Experience with fine-tuning large-scale generative models
Proficiency in PyTorch and distributed training frameworks
(Preferred) Strong background in methods for optimizing model inference (distillation, quantization, sparsity, compression, etc.)
(Preferred) Experience in gathering, processing, and annotating datasets
Nice to have:
Strong background in methods for optimizing model inference (distillation, quantization, sparsity, compression, etc.)
Experience in gathering, processing, and annotating datasets