American Nano Society
Senior Staff Artificial Intelligence Machine Learning Engineer
American Nano Society, Miami, Florida, us, 33222
Overview
Senior Staff Artificial Intelligence Machine Learning Engineer role at American Nano Society. Based remotely in the U.S., part of the Hematology Algorithms & Data Analytics team, reporting to the Director of Hematology. Focus on current on-market instrument support and new product advancements in deep learning, ML, data algorithm development. Responsibilities
Investigate, design, and development of clustering and classification algorithms for blood cell identification and instrument reliability (predictive failures). Algorithm prototyping and development using appropriate machine learning environment. Work with the AI Solutions Architect to drive innovation of new hematological parameters based on data pattern discoveries. Comply with the design and development process documentation as well as all the regulatory aspects of a medical software device. Interface with other functional development and customer support teams to contribute in all aspects of product improvement. Perform statistical analysis of experimental design aimed to improve instrument performance. Qualifications
Advanced degree in a quantitative discipline (e.g. statistics, statistical genetics, imaging science, computational biology, computer science, applied mathematics, applied physics or similar) or equivalent practical experience. Experience developing, training, evaluating, (and perhaps deploying) deep-learning models using popular machine learning frameworks such as PyTorch and TensorFlow/Keras. A firm understanding of deep neural network architectures such as CNNs and GANs. Experience applying deep learning and computer vision techniques for image preprocessing, classification, object detection, segmentation, and feature extraction. Experience developing, training, and evaluating traditional machine learning algorithms such as PCA, SVM, Random Forests, and Gradient Boosting. Well-versed in current deep learning literature and neural network optimization techniques such as batch norm, pruning, model quantization, residual/skip connections, inception modules, F1 and FID metrics etc. Familiar with NVIDIA deep learning ecosystem including CUDA, GPU compute as well as Jetson Nano and Quadro GPU hardware. 5 years or more experience. Technical writing of design documents. Nice to have: Experience working with Docker containers and cloud-based compute environments; other tools: Git, Microsoft Azure, AWS. Experience in hematology domain.
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Senior Staff Artificial Intelligence Machine Learning Engineer role at American Nano Society. Based remotely in the U.S., part of the Hematology Algorithms & Data Analytics team, reporting to the Director of Hematology. Focus on current on-market instrument support and new product advancements in deep learning, ML, data algorithm development. Responsibilities
Investigate, design, and development of clustering and classification algorithms for blood cell identification and instrument reliability (predictive failures). Algorithm prototyping and development using appropriate machine learning environment. Work with the AI Solutions Architect to drive innovation of new hematological parameters based on data pattern discoveries. Comply with the design and development process documentation as well as all the regulatory aspects of a medical software device. Interface with other functional development and customer support teams to contribute in all aspects of product improvement. Perform statistical analysis of experimental design aimed to improve instrument performance. Qualifications
Advanced degree in a quantitative discipline (e.g. statistics, statistical genetics, imaging science, computational biology, computer science, applied mathematics, applied physics or similar) or equivalent practical experience. Experience developing, training, evaluating, (and perhaps deploying) deep-learning models using popular machine learning frameworks such as PyTorch and TensorFlow/Keras. A firm understanding of deep neural network architectures such as CNNs and GANs. Experience applying deep learning and computer vision techniques for image preprocessing, classification, object detection, segmentation, and feature extraction. Experience developing, training, and evaluating traditional machine learning algorithms such as PCA, SVM, Random Forests, and Gradient Boosting. Well-versed in current deep learning literature and neural network optimization techniques such as batch norm, pruning, model quantization, residual/skip connections, inception modules, F1 and FID metrics etc. Familiar with NVIDIA deep learning ecosystem including CUDA, GPU compute as well as Jetson Nano and Quadro GPU hardware. 5 years or more experience. Technical writing of design documents. Nice to have: Experience working with Docker containers and cloud-based compute environments; other tools: Git, Microsoft Azure, AWS. Experience in hematology domain.
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