Senior Software Engineer (CV) Spatial Intelligence CV Group – Taipei

About the Team The Spatial Intelligence CV group builds production-grade computer vision systems that power multimodal and spatially-aware products, from property imagery understanding to 3D scene analysis. We focus on delivering reliable, high-performance services—turning state-of-the-art vision models into scalable, user-facing products. About the Role You will design, optimize, and scale computer vision inference software running on both on-prem and cloud GPU clusters. Beyond writing production-quality Python, you will integrate advanced vision models into end-to-end pipelines that deliver real business value in domains such as architecture, real estate, and interior design. Your work will directly improve system accuracy, throughput, and cost efficiency. In this role, you will Build reusable Python inference pipelines for object detection, segmentation, and image generation at scale. Implement and optimize training/inference flows for YOLO, U-Net, Segment Anything Model (SAM), and Stable Diffusion. Fine-tune diffusion and LoRA-based models for domain-specific generation and augmentation. Develop and integrate solutions in Novel View Synthesis, 6D Pose Estimation, Monocular Depth Estimation, Visual Question Answering (VQA), and Style Transfer. Build Scene Graph pipelines to support space design, dimension measurement, and spatial understanding. Conduct performance profiling and latency/throughput tuning for CV back-ends on GPU clusters. Collaborate with product teams to deliver features such as automated property feature extraction, content tagging, and high-quality image synthesis. You might thrive in this role if you Enjoy turning cutting-edge CV and spatial reasoning research into robust, scalable production services. Have experience across multiple vision tasks and can quickly adapt to new architectures. Are motivated by measurable impact—accuracy gains, inference speedups, or improved user experience. Communicate clearly with cross-functional stakeholders in Mandarin. Minimum Qualifications Master’s degree (or higher) in CS, EE, Applied Math, or related field, or equivalent practical experience. One or more academic or professional projects related to computer vision. Hands-on experience with OpenCV, PyTorch, Git, Docker. Practical experience with YOLO, U-Net, Segment Anything, and Stable Diffusion. Familiarity with at least two of the following: Novel View Synthesis, 6D Pose Estimation, Monocular Depth Estimation, VQA, Style Transfer. Understanding of Scene Graph concepts and their application in spatial reasoning tasks. Bonus Qualifications Experience in classification, segmentation, detection across various datasets. Familiarity with ControlNet or other controllable generation techniques. Publications in top-tier CV/AI conferences (CVPR, ICCV, ECCV, NeurIPS, ICML). Experience operating large-scale CV inference services with measurable performance metrics.