Genus strives to provide beef, dairy and pork producers with superior breeding stock to enable the

production of affordable and nutritious animal protein for consumers. PIC is the swine division with a

mission to pioneer animal genetic improvement to help nourish the world.

PIC currently supplies over 70% of the world’s top pork producers. We directly employ people in 18

countries worldwide and our products are available in more than 50 countries across the globe. We are

a diverse team, ranging from scientists to sales teams, production personnel to customer service, and

are all united by a passion for producing nutritious, affordable pork and a commitment to continuous

improvement.

The Computer Vision Engineer will be responsible for advancing PIC’s high-throughput digital phenotyping platform, leveraging images/video, social network analysis (SNA), and machine learning. This person will ultimately be responsible for assisting in and leading efforts that transform the way phenotypic data is collected and utilized within the genetic improvement program.

• Actively identify disruptive and innovate digital technologies to improve genetic selection processes or customer success.
• Solve challenging problems by utilizing an ecosystem of sensor technology such as video, infrared, RFID, temperature/RH and others, combined with machine learning algorithms, to measure animal behavior, physiology, and health.
• Engage and collaborate with experts across multi-disciplinary fields and organizations to drive innovative technologies.
• Manage immediate need for technology application within the business with long-term digital platform vision.
• Utilize all evolving digital technologies to assess movement, behavior, and health in livestock
• Design, organize and execute projects that lead to development of monitoring tools for automatic detection of behavior, phenotypic and performance traits.
• Utilize existing and new RFID technology to improve the efficiency of data recording.
• Effectively communicate the status and results of research projects to stakeholders.
• The successful candidate will collaborate with a team of statisticians skilled in prediction methodologies using AI and machine learning.
• Anticipates internal and or external business challenges and recommends process, product, or service improvements.
• Communicates complex ideas and influences others to adopt a different point of view.

• Advanced degree in Computer Science, Electrical Engineering, or related field.
• A minimum of 2 years of experience in precision agriculture.
• Experience applying social network analysis (SNA) to large-scale datasets that quantify interactions between individual animals.
• An understanding of statistics, signal processing, time series data, and machine learning.
• Experience building new large-scale datasets used to train and evaluate deep neural networks.
• Familiarity with commonly used methodologies for evaluating neural network performance, e.g., training, testing, validation, leave-one-out, precision-recall, mAP.
• Willingness to learn and apply new techniques to capture, transfer, and interpret large commercial datasets.
• Strong analytical skills with the ability to efficiently collect, organize, analyze, and disseminate significant amounts of information with attention to detail and accuracy.
• Ability to work in and maintain performance expectations in a fast-paced environment.
• Strong project management and communication skills with the ability to communicate in an organized and concise manner.
• Proficient in Microsoft Office and other relevant software applications, including C, C++, Python, Java, MATLAB, and R.
• Ability to travel approximately 15%