Sr. AI Engineer (Generative Vision / Realistic-Novel-View Synthesis)

자격 요건 · 6개

• Master’s degree with 5+ years of relevant industry experience or Ph.D. with 1+ years (or equivalent), with a total of 5–8+ years of hands-on Deep Learning experience across computer vision, machine learning, or robotics domains.
• Strong programming skills : Python required, with C++/CUDA preferred; able to design systems with careful consideration of performance-memory-latency trade-offs.
• Strong foundation in 3D geometry and multiview perception, including camera intrinsics/extrinsics/distortion modeling, coordinate transformations, PnP and bundle adjustment, depth/optical flow estimation, and BEV/occupancy representations.
• Hands-on experience with Temporal/Transformer/Diffusion (at least 1 stack), including large-scale training and hyperparameter tuning.
• Demonstrated expertise in building large-scale video and multiview training pipelines in PyTorch, including distributed training, mixed precision, checkpointing, logging, and replay mechanisms.
• Experience in driving log data engineering(video-LiDAR-CAN alignment, timestamping and sensor drift handling).

우대 사항 · 7개

• Real-world depolyments experiece with Novel-View Synthesis, NeRF, Gaussian Splatting, or Video Diffusion including handling dynamics, occlusion, reflection, and illumination variations.
• Experience with BEV(Bird’s Eye View), Occupancy, or Vector-Space representations within autonomous driving stracks, covering modules such as object detection, lane detection, tracking, and mapping (at least one module)
• Knowledge and practical experience in Self/Weak/Semi-supervised learning, as well as Knowlege Distillation / Teacher-Student frameworks, and synthetic-to-real domain adaptation.
• Familiarity with Embedded or Autonomous SoCs (NVIDIA Orin/TensorRT, TI TDA4/TIDL) with awareness of real-time constraints.
• Hands-on experience with Simulation and Replay frameworks(CARLA, ScenarioRunner, and internal replay tools) for data generation and augmentations.
• Hands-on experience working with large-scale driving datasets such as nuScenes, Waymo, Argoverse2, KITTI-360, as well as proprietary in-house driving logs.
• Demonstrated contributions to the research community, such as publications or reviews in CVPR, ICCV, ECCV, NeurlIPS, or ICLR, and/or competition wins or leadership roles.