Johnson & Johnson Innovative Medicine (JJIM) is recruiting for the role of Senior Engineer, Artificial Intelligence / Computer Vision within our Data Science Analytics & Insights Team. This position may be fully remote within the US.

At Johnson & Johnson, we believe health is everything. Our strength in healthcare innovation empowers us to build a world where complex diseases are prevented, treated, and cured, where treatments are smarter and less invasive, and solutions are personal. Through our expertise in Innovative Medicine and MedTech, we are uniquely positioned to innovate across the full spectrum of healthcare solutions today to deliver the breakthroughs of tomorrow, and profoundly impact health for humanity. Learn more at https://www.jnj.com/.

• Innovative Solution Development: Design, develop, and implement cutting-edge AI and computer vision solutions to address high-priority scientific challenges in biomedical imaging. Your work will directly contribute to advancements in drug discovery and healthcare.
• Develop and optimize state-of-the-art models to extract actionable insights from complex, unstructured biomedical image and video data. Applications include disease identification, patient stratification, and deriving novel biological insights.
• Contribute to the development of scalable core technologies that enable the deployment of advanced computer vision solutions across a variety of imaging modalities, including microscopy, radiology, and video analysis.
• Develop research and product roadmaps for computer vision applications in drug discovery and development, ensuring alignment with organizational goals and scientific priorities.
• Develop and deploy models using containerization technologies such as Docker and Kubernetes to ensure scalable and efficient deployment in production environments.
• Utilize high-performance computing resources and techniques to manage and process large-scale datasets and complex models efficiently.
• Clearly articulate complex technical methods and results to diverse audiences, including scientists, stakeholders, and decision-makers, to facilitate informed decision-making and drive project success.
• Proactively shape and lead internal and external collaborations, participate in cross-functional teams, and engage with key stakeholders to identify opportunities for developing and implementing AI-based solutions.

• Ph.D. in a quantitative discipline (e.g., computer science, artificial intelligence, applied mathematics, electrical engineering or related field) is required. Post-graduate experience is preferred.

• Master's degree with a minimum of 4 years of relevant professional experience is required.

• Demonstrated experience in driving research in computer vision, including the application and development of AI methods and optimization of machine learning models is required.
• Experience with biomedical imaging data such as pathology, radiology, and endoscopy is strongly preferred.
• Proficiency in programming languages such as Python, R, C++, or Java is required.
• Experience with AI frameworks such as PyTorch, TensorFlow, and OpenCV is required.
• Extensive experience with traditional and modern computer vision techniques is required. Examples include object detection (YOLO, SSD, Faster R-CNN), image segmentation (U-Net, Mask R-CNN), image classification (VGG, ResNet, Inception, EfficientNet), and feature extraction (SIFT, SURF, ORB).
• Experience with various machine learning techniques is required. Examples include fully supervised, unsupervised, self-supervised, and weakly supervised learning and specific architectures (Convolutional Neural Networks (CNNs), Vision Transformers (ViTs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), and attention-based networks)
• Strong understanding and experience with machine learning algorithms (such as neural networks, random forests, SVMs, and boosting) along with optimization techniques is required.
• Proficiency in statistical analysis and data handling, including data augmentation, data cleaning, normalization, and handling large-scale image datasets is required.
• Experience working with multi-GPU workloads for training and inference is preferred.
• Experience with deploying models using containerization technologies such as Docker and Kubernetes is preferred.
• Experience with high-performance computing techniques and resources to manage and process large-scale datasets and complex models efficiently is preferred.
• Ability to effectively communicate complex technical concepts and results to a wide range of audiences, including non-technical stakeholders is required.
• High motivation and capability to drive projects forward independently with minimal daily oversight is required.
• A deep passion for leveraging data and AI to drive scientific innovation and improve healthcare outcomes is required.
• Familiarity with drug discovery and clinical development processes, and experience working closely with healthcare professionals is preferred.
• Experience with disease biology in areas such as cancer, immunology, neuroscience, cardiovascular, or infectious disease, enabling the development of targeted AI solutions is a plus.