American Nano Society
Senior Staff Artificial Intelligence Machine Learning Engineer
American Nano Society, Miami, Florida, us, 33222
Wondering what’s within Beckman Coulter?
Take a closer look.
Join a team where you can be heard, be supported, and always be yourself. At Beckman Coulter, a Danaher company, we’re building a culture that celebrates backgrounds, experiences, and perspectives of all our associates. At first glance, you’ll see we are dedicated to advancing and optimizing the laboratory to move science and health care forward for more than 80 years. Look again and you’ll see we are invested in you and you’ll have the opportunity to build a meaningful career, be creative, and try new things with the support you need to be successful. You’ll find the Danaher Business System , which makes everything possible.
And no matter where you look at Beckman Coulter, at the heart of what we do, you’ll witness our shared purpose—helping realize life’s potential—in action. Our work helps improve millions of lives.
We hope you’ll see yourself here, too.
What you find at Beckman Coulter —and within yourself — might just change the world!
Based remotely in the U.S., you will be a part of the Hematology Algorithms & Data Analytics team and report to the Director of Hematology, responsible for current on-market instrument support and new product advancements in the area of deep learning, ML, data algorithm development. If you thrive in a prototyping, collaborative role and want to work to build a world-class data analytics organization—read on!
In this role, you will have the following opportunities, but are not limited to:
Investigate, design, 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 aspect of product improvement
Perform statistical analysis of experimental design aimed to improve instrument performance
The Essential Requirements Of The Job Include
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 tradition machine 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 It would be a plus if you also possess: Experience working with Docker containers and cloud-based compute environments; other tools: Git, Microsoft Azure, AWS Experience in hematology domain Beckman Coulter offers a broad array of comprehensive, competitive benefit programs that add value to associates’ and their families’ lives. Whether it’s a health care program or paid time off, our programs contribute to life beyond the job. Check out our benefits at danaherbenefitsinfo.com. We believe you’ll like what you see! Beckman Coulter is a Danaher Operating Company. As a $20B company with 60,000 associates, ranked #162 on the Fortune 500 and, during the past 25 years, DHR stock has outperformed the S&P 500 Index by more than 5,000 percent. The strength of our results is a testament to our teams and the power of the Danaher Business System. Our Purpose: “Helping Realize Life’s Potential” is what makes us excited to come to work each day! At Danaher, we’re driven by a sense of wonder for what we can create—and what we can become. Here, you’ll accelerate your career while driving innovation that improves lives. You’ll work hard and take risks, with guidance from committed leaders, powerful DBS tools, and the support of a global organization. If you’d like to learn more, read stories from our associates on our Careers Blog or reach out to one of our recruiters. When you join us, you’ll also be joining Danaher’s global organization, where 69,000 people wake up every day determined to help our customers win. As an associate, you’ll try new things, work hard, and advance your skills with guidance from dedicated leaders, all with the support of powerful Danaher Business System tools and the stability of a tested organization.
#J-18808-Ljbffr
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 tradition machine 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 It would be a plus if you also possess: Experience working with Docker containers and cloud-based compute environments; other tools: Git, Microsoft Azure, AWS Experience in hematology domain Beckman Coulter offers a broad array of comprehensive, competitive benefit programs that add value to associates’ and their families’ lives. Whether it’s a health care program or paid time off, our programs contribute to life beyond the job. Check out our benefits at danaherbenefitsinfo.com. We believe you’ll like what you see! Beckman Coulter is a Danaher Operating Company. As a $20B company with 60,000 associates, ranked #162 on the Fortune 500 and, during the past 25 years, DHR stock has outperformed the S&P 500 Index by more than 5,000 percent. The strength of our results is a testament to our teams and the power of the Danaher Business System. Our Purpose: “Helping Realize Life’s Potential” is what makes us excited to come to work each day! At Danaher, we’re driven by a sense of wonder for what we can create—and what we can become. Here, you’ll accelerate your career while driving innovation that improves lives. You’ll work hard and take risks, with guidance from committed leaders, powerful DBS tools, and the support of a global organization. If you’d like to learn more, read stories from our associates on our Careers Blog or reach out to one of our recruiters. When you join us, you’ll also be joining Danaher’s global organization, where 69,000 people wake up every day determined to help our customers win. As an associate, you’ll try new things, work hard, and advance your skills with guidance from dedicated leaders, all with the support of powerful Danaher Business System tools and the stability of a tested organization.
#J-18808-Ljbffr