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Expertise in deep learning frameworks and tensor libraries like PyTorch, Tensorflow, JAX/FLAX. Finally, you will assist in and guide the development of containerized pipeline orchestration infrastructure, including developing and distributing stable base container images, providing advanced profiling and observability, and updating internally maintained versions of deep learning frameworks and their companion libraries like Tensorflow, PyTorch, DeepSpeed, GNNs, Flash Attention and more.
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This is all enabled by cutting edge software stack, the AWS Neuron Software Development Kit (SDK), which includes an ML compiler, runtime and natively integrates into popular ML frameworks, such as PyTorch, TensorFlow and JAX. AWS Neuron is used at scale with customers like Snap, Autodesk, Amazon Alexa, Amazon Rekognition and more customers in various other segments.
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And familiar with commonly used deep learning frameworks (such as PyTorch, TensorFlow, etc). Pursuing a master's or doctoral degree in Computer Science, Computational Linguistics, Artificial Intelligence, or related fields.
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Expert in: MATLAB, TensorFlow, Python, Pandas, NumPy, SciPy, C/C. We seek a scientist to develop unsupervised machine learning algorithms for anomaly detection in optical inspection images of patterned wafers in semiconductor device fabrication.
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They have also contributed to major software projects like TensorFlow and React. Work with top-flight software and hardware and data science engineering talent from places like Google, Amazon, Nvidia, Palantir, and NASA JPL.
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5+ years experience with programming languages such as Python or R, and experience with machine learning libraries (e.g., TensorFlow, PyTorch, scikit-learn). Education in Computer Science, Data Science, Statistics, Computational Linguistics or a related field.
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Demonstrated experience deploying Deep Learning tools like PyTorch or TensorFlow on GPU clusters in. Use containerization and orchestration tools like Docker and Kubernetes to quickly deploy and maintain applications on clustersUse Infrastructure-as-Code (IaC) tools like Terraform or Cloud.
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Machine Learning and Deep Learning: Good understanding of: ML algorithms like linear regression, logistic regression, etc., supervised, unsupervised, and reinforcement learning, AI Frameworks like TensorFlow, PyTorch, scikit-learn etc., Neural network, NLP, computer vision, and predictive analytics.
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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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Expertise in NLP and machine learning frameworks (e.g., TensorFlow, PyTorch). AI and Machine Learning: Utilize state-of-the-art AI and machine learning techniques, including natural language processing (NLP), to train the chatbot for accurate and effective communication.
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Solid understanding of machine learning methods, ideally deep learning, with experience using at least one of the following frameworks: Pytorch, TensorFlow, JAX. The Karczewski Laboratory in the Center for Genomic Medicine (CGM) and Analytic and Translational Genetics Unit (ATGU) at Massachusetts General Hospital (MGH) and the Broad Institute of Harvard and MIT is seeking exceptional computational biology candidates to build tools necessary to visualize and interpret massive data sets of human genetic variation and functional genomic information.
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Our technology stack includes Java and Python as well as a wide range of internal tools built on top of Apache Spark, TensorFlow, and other Big Data technologies. Fluent in some of these machine learning frameworks such as SKLearn, XGBoost, PyTorch, or Tensorflow, and can leverage code as a strategic tool to shape innovative solutions.
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Use programming languages including but not limited to Python, R, SQL, Java or Scala, SQL. Programming Languages: Python, R, SQL, Java or Scala, SQL. Prudential is a multinational financial services leader with operations in the United States, Asia, Europe, and Latin America.
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Technical Skills: Prior experience in programming languages and technologies relevant to AI and DevOps, such as Python, Ruby, and Java. Familiarity with AI frameworks (TensorFlow, PyTorch,) DevOps tools (Docker, Kubernetes, Jenkins,) and cloud environments such as Azure and AWS.
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Utilize: Python, Structured Query Language (SQL), TensorFlow, Cloud Computing & Docker, Machine Learning/Deep Learning algorithms, Probability & Statistics. One (1) year of experience must include utilizing Python, Structured Query Language (SQL), TensorFlow, Cloud Computing & Docker, Machine Learning/Deep Learning algorithms, Probability & Statistics.
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