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Familiarity with AI (Artificial Intelligence) subsets such as Machine Learning, Large Language Models (LLM) and Natural Language Processing (NLP). Bachelor's degree in Computer Science, Engineering, Information Systems, or related field/work experience.
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Create cyber threat intelligence conclusions by using data science and analysis (e.g. outlier detection, gap analysis, normalization, machine learning, automated models, natural language processing, etc.
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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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Our team develops, implements, and maintains recommendation and personalization algorithms for Disney Streaming’s suite of streaming video apps, notably Disney+ and Hulu. As a member of this team you will collaborate across Engineering, Product, and Data teams to apply machine learning methods to meet strategic product personalization goals, explore innovative, cutting edge techniques that can be applied to recommendations, and constantly seek ways to optimize operational processes.
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Familiarity with Natural Language Processing (NLP) techniques, Large Language Model (LLM) applications, and generative AI technologies. 5+ years hands on working experience in using data science framework and knowledge of using all the statistical or AI/ML learning models and recipes to address business use cases.
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Machine Learning: Proficient in machine learning techniques, including supervised and unsupervised learning, deep learning, computer vision, and natural language processing.
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Much of our work contributes to innovative research in the fields of sensor science, signal processing, data fusion, artificial intelligence (AI), machine learning (ML), and augmented reality (AR.
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Harness the power of transformer architecture, a cutting-edge deep learning model widely employed in natural language processing and computer vision, to optimize the language model's performance and efficiency.
$93,400 - $258,500 a yearFull-timeExpandApply NowActive JobUpdated 2 days ago - UpvoteDownvoteShare Job
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A minimum of 1 years experience in data engineering, analytics engineering, or software engineering with an emphasis on large scale data management and processing systems.
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CUDA (Compute Unified Device Architecture) /OpenCL (Open Computing Language) Programming: Develop and optimize applications using CUDA or OpenCL, harnessing the full potential of GPU hardware for parallel processing, high-performance computing, and machine learning on Linux platforms.
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Have familiarity with Voice technologies, including the Nuance Natural Language Platform, SQL databases, and Oracle Session Border Controller (SBC). Full Stack Software Developer with DevOps experience in AWS Cloud based technologies such as IAM, Kinesis, DynamoDB, Amazon Connect, Lambda, Amazon Lex, S3, CloudFront, Contact Lens, Sage Maker.
$76,000 - $185,000 a yearFull-timeRemoteExpandApply NowActive JobUpdated Today - UpvoteDownvoteShare Job
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The Opportunity 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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The role requires both broad and deep architecture, engineering and strategic experience with Microsoft Azure or comparable cloud technologies; specifically, deep technical expertise in cloud-native software defined networking, cloud security, cloud platforms, SRE practices, and Azure Infrastructure as Code modules; Azure Data services, Azure DevOps Repos and Pipelines; Azure Platform as a Service (PaaS); Azure Infrastructure as a Service (IaaS) and supporting technologies.
$129,100 - $229,925 a yearExpandApply NowActive JobUpdated Today - UpvoteDownvoteShare Job
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Basic knowledge of deep learning frameworks and algorithms. Additional factors considered in extending an offer include (but are not limited to) responsibilities of the job, education, experience, knowledge, skills, and abilities, as well as internal equity, alignment with market data, applicable bargaining agreement (if any), or other law.
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Examples will include techniques such as machine learning (ML) supervised and unsupervised learning, regression, neural networks and deep learning, natural language processing, etc.
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