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Preferred Qualifications:- Experience contributing to an open sourced machine learning framework (TensorFlow/PyTorch), experience on improving core machine learning infrastructure.
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You will design and implement high performance AI Training pipeline, data I/O, work with open source teams to identify and resolve issues in popular libraries like Huggingface, Horovod and PyTorch, debug and optimize deep learning training, and provide advanced support for internal AI teams in areas like model parallelism, data parallelism, Zero, automatic mixed precision and kernel fusion.
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RAPIDS, the open-source suite of accelerated data science libraries and primitives, brings accelerated computing to the wider analytics world by combining the performance of NVIDIA GPUs with the ease of use of Python, Apache Spark, and modern C.
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Experience with machine learning and geophysics algorithms or applications such as: deep learning, computer vision, optimization, denoising, signal processing or time-series analysis. Completed coursework in geophysics, linear algebra, signal processing, machine learning, or their equivalent.
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Specialists will develop deep sophistication with the technology behind the Spot product portfolio, and they will use that knowledge to source, close, and ramp software deals in NetApp’s Enterprise segment.
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Apache Spark : Develop the de facto open source standard framework for big data. Modern data analysis employs sophisticated methods such as machine learning that go well beyond the roll-up and drill-down capabilities of traditional SQL query engines.
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Program, set up, and machine quality precision parts per specification, using CNC Swiss machines while working under minimal supervision. Set up and operated Swiss CNC machines and produced parts: generally, worked on more complex parts that required a higher skill level and knowledge of all machining operations.
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This team invents and applies machine learning, data mining, and informational retrieval algorithms to understand, identify, and improve web content discovery. We are a Silicon Valley firm that brings businesses the first open and intelligent Digital Experience Platform (DXP.
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Familiar with one or more open-source deep learning frameworks, such as PyTorch, TensorFlow, PaddlePaddle etc. - Experience in contributing to open-source projects is preferred.
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Prototypes and do proof of concepts (PoC) in one or all the following areas: · LLM, NLP, DL (Deep Learning), Client (Machine Learning), object detection/classification, tracking, etc. · Stay up to date with the latest advancements in LLM, NLP, deep learning, machine learning, and object detection algorithms, and proactively identify opportunities to leverage new technologies for improved solutions.
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Your qualifications You have production experience with building and operating a DataLake, Data Warehouse or Data LakeHousesYou have a taste for big data streaming, storage and processing using open source technologiesYou can demonstrate your understanding of what it means to treat data as a productYou know what are Data Mashes and Data Fabrics and what is critical to make sure for them to bring valueYou are able to learn and adapt.
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Bring structure to a complex collection of open-source, proprietary and in-house Language Models. As a research scientist at Adobe, you will be joining a dynamic team of machine learning, deep learning, natural language processing, and computer systems researchers to build the future of digital experiences.
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Our digital ecosystem combines the power of technology to create beautiful smiles through the integration of AI and machine learning, digital imaging and visualization, biomechanics and material science to develop the Invisalign system, the most advanced clear aligner system in the world; iTero Intraoral Scanners andOrthoCADdigital services.
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You will learn practical modeling skills, as well as software skills to be a successful machine learning engineer. You will identify an applied machine learning problem in the enterprise search and question answering ecosystem.
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Strong familiarity with People Analytics data and technology architecture from data source to ingestion, transformation, and visualization. Utilize Human Capital Management (HCM), people analytics, or enterprise and BI platforms to design, build, test and deploy reports, dashboards, data visualizations, and predictive models based on project requirements.
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