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Employ cutting edge Natural Language Processing (NLP) and Machine Learning (ML) techniques to solve complex natural language problems. Experience with HuggingFace Transformers and other open-source NLP and NLG (natural language generation) modules (ex.
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What Additional Experience Makes A Strong Candidate: 4+ years of production experience with Scala 4+ years of production experience with Micro Services / Streaming Services Experience addressing ML problems, particularly in domains such as Time Series, Computer Vision (CV), Natural Language Processing (NLP), and data mining Proficiency with popular ML frameworks (Xgboost, TensorFlow, PyTorch, etc.
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The Machine Learning Engineer will be responsible for architectural design and planning, advanced data pipelines, model integration and optimization, scalability, performance and research and innovation supporting production AI systems.
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FM Global is seeking a Machine Learning Operations Data Engineer II to join our AI/ML team to support Machine Learning Engineering, working very closely with Data Science, Data Engineering, Subject Matter Experts and Solution Architecture teams.
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Role Overview As a Machine Learning Operations Data Engineer II you will develop platform tooling, deploy data science models to production and monitor production performance.
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At least one of the following: GCP Professional Machine Learning Engineer, AWS Certified Machine Learning Engineer. The candidate should have subject matter expertise in designing and implementing machine learning pipelines including a deep understanding of core ML frameworks such as PyTorch, TensorFlow or Scikit-learn and 3rd party ML platforms from Palantir, Dataiku, DataRobot, Alteryx and other vendors.
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In addition, the candidate should have a strong understanding and experience with the integrated machine learning environments provided in Azure, AWS and GCP, along with emerging experience with Generative AI ML technical architectures that include pre-trained LLM and diffusion models such as LLaMA, ChatGPT, Gemini, Stability AI and Claude as well as Vector Databases and orchestration tools such as LangChain.
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Drives the development and implementation of advanced machine learning models and algorithms to solve complex healthcare problems, leveraging techniques such as predictive modeling, deep learning, and natural language processing.
$124,372.5 - $247,200 a yearFull-timeExpandApply NowActive JobUpdated Today - UpvoteDownvoteShare Job
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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.
$127,300 - $229,300 a yearFull-timeExpandApply NowActive JobUpdated Today - UpvoteDownvoteShare Job
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Deep knowledge of data mining, machine learning, natural language processing, or information retrieval. "Experience in data engineer , Data Analytics; Big Data Technologies Expert in Python.
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The Vertex AI Machine Learning Engineer will be responsible for developing, deploying, and managing machine learning models using Google Cloud's Vertex AI platform. Google Cloud Professional Machine Learning Engineer certification.
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Artificial intelligence and predictive analytics in health (i.e. Machine Learning, Large Language Models, Natural Language Processing ) DBMI has research and/or service programs in the following areas: Clinical informatics, clinical research informatics, biomedical data modeling and ontologies, biomedical natural language processing and information retrieval, medical artificial intelligence, privacy technology, global health informatics, equity in informatics, and translational bioinformatics.
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Collaborate with data scientists and analysts to support machine learning initiatives and advanced analytics. You will be responsible for designing, developing, and maintaining scalable data pipelines, optimizing data flow, and enabling advanced analytics and machine learning initiatives.
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Research and Innovation: Stay up to date with the latest advancements in the field of natural language processing and machine learning. Model Training and Evaluation: Train and fine-tune language models using appropriate machine-learning frameworks and tools.
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They must have knowledge and experience with "black box" AI capabilities for traditional tasks such as image and video recognition, speech to text, language translation and chatbots, along with a deep understanding of the intellectual property and data privacy issues inherent in using pre-trained generative AI platforms.
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machine learning natural language engineer data jobs Company: Veradigm
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