Senior Staff Digital World System Engineer
XPENGSanta Clara, California, United States · Posted 3 months agoDescription
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Be responsible for the overall architecture design and technological evolution of the next-generation synthetic data and Digital World generation system, supporting large-scale model training, simulation and evaluation in the directions of autonomous driving, robotics and embodied intelligence.
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Lead the construction of core capabilities in directions such as Digital City, Digital Twin, and World Model, and build a high-fidelity, highly scalable virtual world generation and closed-loop simulation system.
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Be responsible for the R&D of algorithms and systems related to 3D/4D scene reconstruction, including multi-view 3D reconstruction, Gaussian Splatting, NeRF, SLAM/SfM, spatiotemporal scene modeling and other directions, to build large-scale spatial understanding and reconstruction capabilities.
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Build a synthetic data production engine for perception, prediction, planning, VLA and robot model training, realizing high-quality, high-efficiency and low-cost data generation and delivery.
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Explore the application of cutting-edge technologies such as Generative AI, Diffusion, World Model and Physics Simulation in the field of simulation and data generation, and promote the evolution of data closed-loop towards automation and intelligence.
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Build a closed-loop simulation system for Digital City, realizing capabilities such as problem scenario reproduction, model playback verification, long-tail problem mining and automated evaluation.
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Collaborate in depth with teams of autonomous driving algorithms, robotics, simulation platforms, data Infra and training platforms to promote the integration and large-scale application of synthetic data and real data systems.
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Define synthetic data quality standards and evaluation systems, continuously optimize authenticity, diversity, generalization and Sim2Real effects, and improve the benefits of model training.
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Be responsible for the construction of synthetic data platform and team, promote the platformization, engineering and large-scale landing of core technologies, and form long-term evolution capabilities.
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Master's degree or above in Computer Science, Artificial Intelligence, Robotics, Computer Vision, Computer Graphics or related majors.
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Have R&D experience in related fields such as synthetic data, simulation systems, 3D vision, neural rendering or embodied intelligence.
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In-depth understanding of technologies related to 3D reconstruction and spatial modeling, including but not limited to SfM, SLAM, NeRF, Gaussian Splatting, Differentiable Rendering, Photogrammetry and other directions.
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Candidates with relevant project experience in Digital Twin, Digital City, simulation platforms or large-scale virtual scene generation are preferred.
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Familiar with cutting-edge technology directions such as multi-modal large models, World Model, Generative AI, Diffusion and Reinforcement Learning, and have an in-depth understanding of data-driven model training systems.
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Have R&D experience in large-scale distributed systems, data generation platforms or high-performance rendering systems, and be able to promote the engineering landing of complex systems.
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Candidates familiar with Unreal Engine, Unity, Omniverse, physical simulation engines or graphics rendering pipelines are preferred.
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Have excellent system architecture capabilities and cross-team collaboration capabilities, and be able to promote complex technical projects from research to production landing.
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Have continuous enthusiasm and technical exploration capabilities in directions such as embodied intelligence, World Model and Digital World generation.
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A fun, supportive and engaging environment.
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Infrastructures and computational resources to support your work.
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Opportunity to work on cutting edge technologies with the top talents in the field.
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Opportunity to make significant impact on the transportation revolution by the means of advancing autonomous driving.
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Competitive compensation package.
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Snacks, lunches, dinners, and fun activities.
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