Principle Applied Scientist - Computer Vision

You will be responsible for leading the research, design, and development of advanced computer vision models for tasks such as object detection, tracking, segmentation, OCR, scene understanding, and 3D vision. You will need to translate business requirements into scalable scientific solutions by utilizing cutting-edge deep learning and classical computer vision techniques. Additionally, you will design and conduct experiments to assess the performance, robustness, and accuracy of computer vision models in real-world production settings. Collaborating with cross-functional teams, including software engineering, product, and data teams, to integrate vision models into applications is also a key part of your role. Moreover, you will drive innovation through internal IP generation, such as patents and publications, and contribute to the long-term AI/ML roadmap. Providing scientific and technical leadership, mentoring junior scientists, and reviewing designs and architectures will be essential. Staying updated with the latest advancements in AI, deep learning, and computer vision through academic and industrial research is crucial to ensure alignment with industry best practices and emerging technologies. Qualifications: - M.S. or Ph.D. in Computer Science, Electrical Engineering, or a related field with a focus on Computer Vision or Machine Learning. - 5+ years of practical experience in developing and deploying production-grade computer vision models. - Profound theoretical knowledge and practical experience with deep learning frameworks like PyTorch, as well as model architectures such as CNNs, Vision Transformers, and Diffusion Models. - Experience in handling large-scale datasets, training pipelines, and evaluating model performance metrics. - Proficiency in Python and scientific computing libraries like NumPy, OpenCV, and scikit-learn. - Experience with model optimization for edge deployment (e.g., ONNX, TensorRT, pruning/quantization) is advantageous. - Strong communication skills, both written and verbal, along with a history of mentoring and collaboration. Preferred Qualifications: - Experience working with computer vision in real-time systems such as AR/VR, robotics, automotive, or surveillance. - Published research papers in esteemed conferences like CVPR, ICCV, NeurIPS, etc. - Exposure to MLOps or ML model lifecycle in production settings. - Familiarity with cloud platforms like AWS/GCP/Azure, containerization tools such as Docker and Kubernetes, and basic bash scripting.,