The Senior Machine Learning Engineer performs research and testing to develop machine learning algorithms and predictive models. Designs and develops scalable solutions using AI tools and machine-learning models. Being a Senior Machine Learning Engineer conducts model training and evaluation. Utilizes big data computation and storage tools to create prototypes and datasets. In addition, Senior Machine Learning Engineer integrates, tests, tunes, and monitors solutions. Proficient with multiple AI tools such as Python, Java, or R and machine learning frameworks like Spark, TensorFlow, or sciket-learn. Requires a master's degree in computer science, mathematics, engineering or equivalent. Typically reports to a manager or head of a unit/department. Being a Senior Machine Learning Engineer work is generally independent and collaborative in nature. Contributes to moderately complex aspects of a project. Working as a Senior Machine Learning Engineer typically requires 4 -7 years of related experience.
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The Senior Machine Operator uses computer-controlled equipment to select position and secure automatic or semi-automatic machines. Sets up and operates a production machine in accordance with established procedures and guidelines. Being a Senior Machine Operator may assist with training other machine operators. Controls and adjusts machine settings to complete more complex tasks. In addition, Senior Machine Operator may calculate and assist with numeric controlled programming. Requires a high school diploma or its equivalent. Typically reports to a supervisor/manager. Being a Senior Machine Operator works independently within established procedures associated with the specific job function. Has gained proficiency in multiple competencies relevant to the job. Working as a Senior Machine Operator typically requires 3-5 years of related experience, or may need 2 years experience with additional specialized training and/or certification.
Machine learning (ML) is the scientific study of algorithms and statistical models that computer systems use to effectively perform a specific task without using explicit instructions, relying on patterns and inference instead. It is seen as a subset of artificial intelligence. Machine learning algorithms build a mathematical model of sample data, known as "training data", in order to make predictions or decisions without being explicitly programmed to perform the task.:2 Machine learning algorithms are used in a wide variety of applications, such as email filtering, and computer vision, where it is infeasible to develop an algorithm of specific instructions for performing the task. Machine learning is closely related to computational statistics, which focuses on making predictions using computers. The study of mathematical optimization delivers methods, theory and application domains to the field of machine learning. Data mining is a field of study within machine learning, and focuses on exploratory data analysis through unsupervised learning. In its application across business problems, machine learning is also referred to as predictive analytics.
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