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We are interested in a variety of topics including large-scale distributed systems, stream processing, edge computing, applied machine learning and AI, big graphs, natural language processing, big data management, and heterogenous data analytics.
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As a Computer Scientist/Engineer on the Fraud Team, you will be at the forefront of developing and executing an innovative strategy utilizing Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), Natural Language Processing (NLP), Large Language Models (LLM), and Multi-Agent Systems to combat a variety of fraudulent activities.
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Develop, enhance, and continuously optimize GenAI algorithms for tasks such as data content generation, recommendation systems, and natural language processing (NLP). Experience with multiple cloud-based data platforms (e.g., Microsoft Azure, Google Cloud, SAP Datasphere or SAP Datawarehouse cloud) and an expert ability to use various tools and frameworks that support data engineering tasks, such as Databricks, Snowflake, Spark, Kafka, Airflow, Azure, GCP, etc.
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You have experience in backend engineering, data infrastructure, search infrastructure or natural language processing/machine learning. You have experience with large scale data processing technologies (e.g. Spark, Flink, Kafka, Airflow, YARN/Hadoop.
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Expert level of Advanced and Predictive Analytical Methods e.g., Simulation, Design of Experiments, Genetic Algorithms, Ensemble Methods, Nave Bayes, Neural Networks, regression, image processing, natural language processing.
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In conjunction and close collaboration with the Natural Language Processing Specialist and other personnel from across the libraries, this position provides leadership to the Libraries’ efforts to build foundational AI services for all UF students, faculty, and staff interested in incorporating AI technology and techniques in their discipline-specific teaching and research.
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Experience with the latest techniques in natural language processing including vector databases, fine-tuning LLMs, and deploying LLMs with tools such as HuggingFace, Langchain, and OpenAI.
$124,800 - $220,800 a yearFull-timeExpandApply NowActive JobUpdated Today - UpvoteDownvoteShare Job
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This role will involve hands-on work in one or more applied AI technology areas such as ML, deep learning and natural language processing, or other advanced technologies. Experience working with large data sets using big data processing frameworks (e.g. Azure Data Lake, HDFS/Hadoop, Spark or other cluster computing/MapReduce frameworks) and/or public cloud infrastructure (Azure, AWS, Google Cloud) for building, evaluating, or deploying machine learning or NLP models is a plus.
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In this role, you will play a critical role in developing and implementing Natural Language Processing (NLP) solutions that support virtual assistants, document understanding, personalized search & recommendation experiences, etc.
$82,000 - $236,500 a yearFull-timeRemoteExpandApply NowActive JobUpdated Yesterday - UpvoteDownvoteShare Job
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Familiarity with OpenAI / LLM models for natural language processing tasks. Proficiency in Spark/Databricks for big data processing. Utilize Python, Spark, and other relevant technologies to build scalable and efficient ML solutions.
$130,000 - $160,000Full-timeExpandApply NowActive JobUpdated Today - UpvoteDownvoteShare Job
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At least one year of experience in the Machine Learning or AI domain, working with technology infrastructure related to Deep Learning, Large Language Models (LLM), Generative AI (GAI), Natural Language Processing (NLP), or other areas.
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Develop AI-based systems for Natural Language Processing (NLP) Hands-on experience in applying Natural Language Processing solutions to challenging real-world problems.
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Good foundation in Machine Learning (ML), Deep Learning, Large Language Models (LLM) and Natural Language Processing (NLP). Tagged as: Industry , Language Modeling , Machine Learning , Natural Language Processing , NLP , Unspecified.
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As needed, collaborate with internal and external stakeholders to identify object detection, optical character recognition, automation, predictive modeling, pattern analysis, natural language processing, fraud detection, and other business cases for using ML.
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And JavaExperience using libraries and frameworks such as TensorFlow, PyTorch, Spark ML/MLlib, and JupyterExcellent verbal and written communication skillsAbility to multi-task and stay flexible in a dynamic work environment.
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