Senior Staff Digital World System Engineer

@ XPENG

Senior Staff Digital World System Engineer

Posted 2 months ago

This job is still taking applications, but it's been up a while.

About the job

XPENG is a leading smart tech company innovating in AI, autonomous driving, and robotics, focusing on intelligent mobility and cutting-edge R&D in automation.

Requirements

Master's degree or above in relevant field
Experience in synthetic data, simulation systems
Knowledge of 3D reconstruction, spatial modeling
Project experience in Digital Twin or virtual scenes
Familiar with large models, Generative AI

Qualifications

Strong system architecture skills
Cross-team collaboration ability
Passion for embodied intelligence and digital worlds
Experience with Unreal, Unity or Omniverse
Promotes research to production transition

Full job description

XPENG is a leading smart technology company at the forefront of innovation, integrating advanced AI and autonomous driving technologies into its vehicles, including electric vehicles (EVs), electric vertical take-off and landing (eVTOL) aircraft, and robotics. With a strong focus on intelligent mobility, XPENG is dedicated to reshaping the future of transportation through cutting-edge R&D in AI, machine learning, and smart connectivity.

Job Responsibilities

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.
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.
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.
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.
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.
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.
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.
Define synthetic data quality standards and evaluation systems, continuously optimize authenticity, diversity, generalization and Sim2Real effects, and improve the benefits of model training.
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.

Requirements

Master's degree or above in Computer Science, Artificial Intelligence, Robotics, Computer Vision, Computer Graphics or related majors.
Have R&D experience in related fields such as synthetic data, simulation systems, 3D vision, neural rendering or embodied intelligence.
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.
Candidates with relevant project experience in Digital Twin, Digital City, simulation platforms or large-scale virtual scene generation are preferred.
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.
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.
Candidates familiar with Unreal Engine, Unity, Omniverse, physical simulation engines or graphics rendering pipelines are preferred.
Have excellent system architecture capabilities and cross-team collaboration capabilities, and be able to promote complex technical projects from research to production landing.
Have continuous enthusiasm and technical exploration capabilities in directions such as embodied intelligence, World Model and Digital World generation.

What do we provide:

A fun, supportive and engaging environment.
Infrastructures and computational resources to support your work.
Opportunity to work on cutting edge technologies with the top talents in the field.
Opportunity to make significant impact on the transportation revolution by the means of advancing autonomous driving.
Competitive compensation package.
Snacks, lunches, dinners, and fun activities.

The base salary range for this full-time position is $203,450-$344,300, in addition to bonus, equity and benefits. Our salary ranges are determined by role, level, and location. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position across all US locations. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training.

We are an Equal Opportunity Employer. It is our policy to provide equal employment opportunities to all qualified persons without regard to race, age, color, sex, sexual orientation, religion, national origin, disability, veteran status or marital status or any other prescribed category set forth in federal or state regulations.

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