About the job
Join our groundbreaking research team as we revolutionize the future of physical AI through groundbreaking generative models. We are now hiring Research Scientists to join our Cosmos team! As a Research Scientist specializing in Generative AI for Physical AI, you'll be at the forefront of developing next-generation algorithms that bridge the gap between virtual and physical realms. You'll work with state-of-the-art technology and have access to massive computational resources to bring your ideas to life.
Responsibilities
Pioneer revolutionary generative AI algorithms for physical AI applications, with a focus on advanced video generative models and video-language models
Architect and implement sophisticated data processing pipelines that produce premium-quality training data for Generative AI and Physical AI systems
Design and develop cutting-edge physics simulation algorithms that enhance Physical AI training
Scale and optimize large-scale training systems to efficiently harness the power of 20,000+ GPUs for training foundation models
Author influential research papers to share your groundbreaking discoveries with the global AI community
Drive innovation through close collaboration with research teams, diverse internal product groups, and external researchers
Build lasting impact by facilitating technology transfer and contributing to open-source initiatives
Qualifications
Minimum
PhD in Computer Science, Computer Engineering, Electrical Engineering, or related field (or equivalent experience).
Deep expertise in PyTorch and related libraries for Generative AI and Physical AI development
Strong foundation in diffusion, vision language and reasoning models and their applications
Proven experience with reinforcement learning algorithms and implementations
Robust knowledge of physics simulation and its integration with AI systems
Demonstrated proficiency in 3D generative models and their applications
Preferred
Publications or contributions to major AI conferences (ICLR, NeurIPS, ICML, CVPR, ECCV, SIGGRAPH, ICCV, etc.)
Experience with large-scale distributed training systems
Background in robotics or physical systems
Open-source contributions to prominent AI projects
History of successful research-to-product transitions