AI Scientist, Vision AI

Job Description:• Research, design, and implement cutting-edge computer vision models for tasks such as image classification, object detection, semantic/instance segmentation, and video understanding. • Develop and optimize generative vision models, including text-to-image, text-to-video, and image-to-video approaches. • Train, fine-tune, and evaluate large-scale vision foundation models, adapting them to healthcare-specific applications. • Collaborate with AI scientists, engineers, and product teams to integrate vision AI capabilities into Artisight’s platform.• Stay at the forefront of vision AI and multimodal learning research, bringing innovations from the research community into production applications. • Document and share research outcomes through technical reports, internal presentations, and where appropriate, external publications. • Work at the intersection of research and application — designing novel vision models and deploying these technologies into real-world healthcare environments. Requirements:• M.S. or Ph.D. in computer science, electrical engineering, applied AI, machine learning, or related discipline.• Demonstrated expertise in computer vision research, evidenced by open-source contributions or peer-reviewed publications (e.g., CVPR, ICCV, ECCV, NeurIPS, ICML, ICLR). • Hands-on experience with one or more of: Image classification and object detection; Image segmentation (semantic, instance, or panoptic); Video classification and temporal modeling; Text-to-Image / Text-to-Video generation; Image-to-Video or video synthesis. • Strong knowledge of deep learning methods (transformers, diffusion models, CNNs, self-supervised learning, multimodal architectures).• Proficiency in frameworks such as PyTorch or TensorFlow, with experience in large-scale vision model training. • Familiarity with deployment tools such as ONNX, NVIDIA Triton, or similar inference platforms. • Strong problem-solving skills and the ability to clearly communicate research insights across disciplines. • Nice to haves: Experience with multimodal learning (vision + audio + text); Familiarity with 3D vision, medical imaging, or spatiotemporal models; Experience with real-time video analysis and low-latency deployment; Contributions to open-source vision projects (e.g., Detectron2, MMDetection, Segment Anything, Stable Diffusion, OpenMMLab).Benefits: Apply tot his job