About the role
Design and build automated pipelines for converting reality capture data (photogrammetry, LiDAR point clouds, depth camera scans, 360-degree imagery) into physics-ready 3D simulation assets with accurate geometry, collision meshes, material properties, and articulation definitions. Develop and maintain toolchains for physics-ready 3D asset generation, including mesh optimization, UV unwrapping, PBR material assignment, collision hull generation, mass/inertia parameter estimation, and annotation of semantic and functional properties. Integrate reality capture hardware and software workflows (e.g., NeRF, Gaussian splatting, structured light scanning, photogrammetry reconstruction) with the simulation platform's asset ingestion pipeline. Build 3D reconstruction workflows that enable rapid creation of simulation environments from real-world facility scans, supporting robotics deployment planning, testing, and validation. Create and maintain asset toolchains supporting industry-standard formats (USD/OpenUSD, glTF, FBX, OBJ) with appropriate physics and simulation metadata for import into robotics simulation engines. Develop synthetic data generation pipelines that leverage high-fidelity 3D assets to produce training data for perception, manipulation, and navigation models, including domain-randomized variations of materials, lighting, object placement, and camera viewpoints. Collaborate with robotics engineers, ML researchers, and perception scientists to define asset fidelity
requirements, validate simulation-to-reality visual and physical accuracy, and iterate on asset quality based on downstream model performance. Implement quality assurance and validation workflows for 3D assets, including automated checks for mesh integrity, physics parameter consistency, rendering fidelity, and simulation stability. Review code and technical designs to ensure adherence to team standards for 3D pipeline performance, asset management, and data integrity. Remain current in 3D reconstruction, neural rendering, and asset generation research, proactively evaluating new techniques (e.g., generative 3D models, neural radiance fields, 3D Gaussian splatting) for integration into the platform. Bachelor's Degree in Computer Science or related technical field AND 4+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python Master's Degree in Computer Science or related technical field AND 6+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR Bachelor's Degree in Computer Science or related technical field AND 8+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR equivalent experience. Experience with 3D reconstruction pipelines, photogrammetry, LiDAR processing, or neural scene representation methods (NeRF, 3D Gaussian splatting). Proficiency with 3D content creation and processing tools (Blender, Fusion, Maya, Houdini, MeshLab, Open3D, or equivalent). Experience with USD/OpenUSD, OBJ, STL, or equivalent 3D interchange formats and their integration with simulation or rendering engines. Background in physically based rendering (PBR), material authoring, or real-time graphics pipeline development. Experience building synthetic data generation pipelines for training computer vision or perception models. Familiarity with robotics simulation platforms (Isaac Sim, Gazebo, MuJoCo, or equivalent) and their asset
