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Proficient in using at least one mainstream deep learning framework such as TensorFlow/PyTorch, understanding distributed training, distillation acceleration, and other implementation methods.
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Degree in a quantitative discipline such as Predictive Analytics, Statistics, Mathematics, or Computer Science with coursework in Machine Learning, Deep Learning, and Natural Language Processing; Masters/PhD preferred.
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Working with Sagemaker, Tensorflow, Pytorch, Triton, Spark, or equivalent large-scale distributed Machine Learning technologies on a modern containerized deployment stack using Kubernetes, Spinnaker, and other technologies.
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Optimize deep learning workflows for execution on NVIDIA GPUs using frameworks such as TensorFlow, PyTorch, or MXNet. Proficiency in programming languages such as Python and Julia, and experience with machine learning packages (e.g., PyTorch, TensorFlow, Jax.
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1+ years of experience in designing and implementing Natural Language Processing (NLP) models using machine learning techniques including neural networks, deep learning and transformer architectures.
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As a Senior/Staff Machine Learning Engineer you are someone who has significant expertise in modeling, statistics and programming. A PhD degree in Machine Learning or Deep Learning is a massive plus.
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Develop deep learning-based methodologies for characterizing single cell and spatial transcriptomics datasets to infer features critical for cancer immunology drug discovery. + Stay up to date on state-of-the-art machine learning methodologies and their applications in cell engineering and drug discovery.
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Develop and implement scalable and reproducible machine learning pipelines for 3D shape quantification on data from microscopy image-based assays. We seek a motivated, knowledgeable, and team-oriented scientist with excellent machine learning knowledge, interested in applying these skills to biology as part of the Computational Cell Science team in the Allen Institute for Cell Science.
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The Company creates quality books and ebooks, print and technology-based learning programs for pre-K to grade 12, classroom magazines and other products and services that support children's learning both in school and at home.
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Proficiency in programming languages such as Python and Julia, and experience with developing large scale models using machine learning packages (e.g., PyTorch, TensorFlow, Jax). Proven track record of research and publications in machine learning, particularly in the areas of diffusion models, generative models, NLP, or related topics.
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Experience in building, deploying, maintaining and sunsetting large-scale machine learning models and systems in production environments using TensorFlow, Keras, PyTorch, Scikit-Learn, AWS, Azure, Google Cloud.
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5+ years of experience in data analysis or data science, with 3+ years focusing on machine learning problems, ideally in a relevant space (KYC, sanctions detection, anti-fraud detection, treasury management, crypto/blockchain data science.
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The SSS will primarily focus on implementing Social Emotional Learning sessions with individual scholars or small groups, and will be responsible for intervening, redirecting, and providing support to scholars facing behavioral challenges.
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We are looking for a passionate, talented, and resourceful Applied Scientist in the field of LLM, Artificial Intelligence (AI), Natural Language Processing (NLP), Recommender Systems and/or Information Retrieval, to invent and build scalable solutions for a state-of-the-art context-aware conversational AI. A successful candidate will have strong machine learning background and a desire to push the envelope in one or more of the above areas.
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Conduct advanced quantitative research, using machine learning (ML) and natural language processing (NLP) techniques to understand patterns in large volumes of data, identify relationships, detect data anomalies and classify data.
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learning job Title: entry level in Seattle, WA
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