AI Engineer - Neural Scene Reconstruction

Overview: As an AI Engineer specializing in Neural Scene Reconstruction, you will contribute to the R&D of advanced 3D reconstruction and neural rendering technologies that transform real-world driving data into realistic, interactive simulation environments. Working closely with senior researchers and engineers, you will develop, train, and evaluate models for high-fidelity scene reconstruction, novel-view synthesis, and sensor rendering. This role offers the opportunity to explore cutting-edge 3D vision techniques, work with large-scale multimodal driving datasets, and help build realistic simulation environments for training and validating autonomous driving systems. Responsibilities Develop and improve neural scene reconstruction models using 3DGS, NeRF, and other learned 3D representations to reconstruct realistic driving environments. Implement dynamic scene reconstruction techniques, modeling static environments and moving traffic agents while handling occlusions, incomplete observations, and challenging environmental conditions. Develop neural rendering and novel-view synthesis methods to generate realistic camera observations from reconstructed 3D scenes. Contribute to multimodal sensor rendering, including camera, LiDAR, and radar simulation, to support realistic virtual testing environments. Build scalable reconstruction and rendering pipelines for efficient experimentation and large-scale processing of real-world driving datasets. Train and optimize reconstruction models, improving geometric accuracy, visual fidelity, temporal consistency, and computational efficiency. Develop evaluation tools and benchmarks to measure reconstruction quality, sensor fidelity, and real-to-sim consistency. Support interactive scene simulation, enabling viewpoint changes, scene editing, agent manipulation, and counterfactual scenario generation. Collaborate with simulation, perception, and autonomous driving teams to integrate reconstructed scenes into training, testing, and closed-loop evaluation workflows. Explore emerging research directions, including generative reconstruction, video diffusion, neural rendering, and world models. Requirements 5+ years of practical experience in machine learning, computer vision, computer graphics, robotics, or related fields. Bachelor's or Master's degree in Computer Science, Computer Vision, Robotics, Electrical Engineering, or a related discipline, or equivalent practical experience. Solid understanding of 3D computer vision fundamentals, including multi-view geometry, camera models, coordinate transformations, and 3D representations. Familiarity with neural rendering or 3D reconstruction techniques, such as 3D Gaussian Splatting, NeRF, or related methods. Strong programming skills in Python and hands-on experience with deep learning frameworks, particularly PyTorch. Experience training, evaluating, and debugging deep learning models using real-world datasets. Familiarity with GPU-accelerated computing, differentiable rendering, or 3D vision libraries. Good analytical and problem-solving skills, with the ability to conduct systematic experiments and interpret model performance. Ability to collaborate effectively with researchers and engineers in a fast-paced R&D environment. Preferred Qualifications Hands-on experience with 3D Gaussian Splatting, NeRF, or other neural scene reconstruction methods. Experience with dynamic or 4D reconstruction, moving object modeling, scene decomposition, or spatiotemporal representations. Familiarity with neural rendering frameworks such as gsplat, Nerfstudio, or similar tools. Knowledge of structure-from-motion, SLAM, camera calibration, pose estimation, or LiDAR point cloud processing. Experience with C++, CUDA, or GPU rendering optimization. Familiarity with generative models, video diffusion, or world models for scene reconstruction and novel-view synthesis. Knowledge of sensor simulation, including camera, LiDAR, or radar rendering. Experience with autonomous driving datasets, multimodal sensor data, or large-scale outdoor scene reconstruction. Familiarity with distributed training, large-scale data processing, or GPU computing infrastructure. Understanding of simulation fidelity, real-to-sim transfer, or closed-loop autonomous driving evaluation. Publications, open-source contributions, or research projects in computer vision, graphics, machine learning, robotics, or related fields. 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