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Provide technical expertise and support to POD teams in the development and implementation of machine learning and AI solutions, leveraging Azure, Databricks, and MLflow. As a Machine Learning Engineer supporting POD teams, you will play a pivotal role in providing technical expertise and guidance to ensure the successful development and delivery of machine learning and AI solutions.
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Assist in the development and optimization of machine learning algorithms and models, providing guidance on best practices and methodologies. We are seeking a skilled Machine Learning Engineer with expertise in Azure, Databricks, and MLflow, as well as strong full-stack capabilities, including experience with React.
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Experience with Azure cloud services, particularly Azure Machine Learning and Azure Databricks. 5+ years of experience in machine learning engineering, data science, or a related field.
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More recently, NVIDIA has orchestrated the development and adoption of culture-changing technologies in Artificial Intelligence and Cryptography. We collaborate with a broad cross section of teams at NVIDIA ranging from DL research teams to CUDA Kernel and DL Framework development teams, to Silicon Architecture Teams.
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Proven experience in instructional design and eLearning development specifically for Beckman Coulter or similar organizations. Familiarity with adult learning theories and instructional design models (e.g., ADDIE, STAR), specifically within the context of Beckman Coulter.
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Other Benefits Complementary stand-by cruises, employee discounts on confirmed cruises, plus special rates for family and friends Personal and professional learning and development resources including tuition reimbursement On-site preschool program, wellness center, and health clinic at our Miami campus.
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Your responsibilities will include collaborating closely with product teams, contributing your expertise in machine learning technologies, and leveraging your full-stack capabilities to support the implementation of frontend components using React.
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Familiarity with Front-end web development technologies (React, Vue.js, Angular, etc.) As a Google Vertex Full Stack Software Engineer, you will be responsible for developing and implementing machine learning models using Google Vertex AI and developing consumable API microservices using Typescript, Node.js, Express.
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At least 10 years of industrial/academic experience advancing the state-of-the-art machine learning-based research through demonstrable, verifiable technical results in the area of natural language processing, human language understanding, computational linguistics.
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Assist in the deployment of machine learning models into production environments, implementing monitoring and logging mechanisms for performance tracking. Collaborate with frontend developers to support the implementation of responsive and user-friendly web interfaces using React, integrating machine learning capabilities seamlessly into frontend applications.
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Knowledge of machine learning algorithms, statistical modeling, and data structures. Proficiency in Python for machine learning and data analysis. Strong problem-solving mindset and a proactive attitude towards learning and self-improvement.
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About Catapult Learning Catapult Learning, a division of FullBloom, provides intervention programs for students and professional development solutions for teachers in K-12 schools, executed by a team of experienced coaches.
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Have functional knowledge related to some of the following: modeling knowledge of financial risks and derivative products (e.g., equity, FX, commodities, credit and interest rates), risk management, model development, model validation, advanced analytics (e.g., machine learning techniques.
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Catapult Learning, a division of FullBloom, provides intervention programs for students and professional development solutions for teachers in K-12 schools, executed by a team of experienced coaches.
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Teacher Openings: Infant Teacher Toddler Teacher Early Preschool Teacher Preschool Teacher GA Pre-K Teacher School-Age Teacher Teacher Responsibilities: Implement our award-winning curriculum in a way that meets each child's unique learning style.
$13 - $20 an hourFull-timeExpandApply NowActive JobUpdated 4 days ago
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The summer had economists from around the globe embroiled in a debate about a possible recession coming in the next few years (or months). As of October 2022, the U.S. Labor Department data put the current inflation rate at 7.7%. The recent layoffs in the tech industry are just the first of what is soon to be a string of cutbacks by companies looking to save costs. For recruiters, this means freezes in hiring and fewer openings. It will also include the uphill task of finding the best candidates for them from the coming influx of recently laid-off job seekers. Now is probably a good time to brace for tough times in the next few years in the talent acquisition industry. To survive and thrive recruiting in a recession, here are some hard truths you will need to accept.
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Collaborative Recruiting: The Key to a Better Talent Acquisition Strategy
Talent acquisition is a multi-stage process where candidates undergo various application steps before getting hired. The unfortunate reality is that it is a labor-intense system, with the hiring manager and recruiter often handling all of the work on their own. Ask any one of them, and you will hear about the overabundance of applications and the demanding task of filtering through them to find the best candidates. The quality of talent suffers under the weight of all that work on one person's hands. It's not easy, but as many companies are starting to realize, there is a better way. The future of talent acquisition lies in collaborative recruiting!