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New York City (Hybrid On-Site): $165,100 - $188,500 for Senior Machine Learning Engineer. As a Capital One Machine Learning Engineer (MLE), you'll be part of an Agile team dedicated to productionizing machine learning applications and systems at scale.
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As a Capital One Machine Learning Engineer (MLE), you'll be part of an Agile team dedicated to productionizing machine learning (ML) applications and systems at scale. Lead Machine Learning Engineer, Finance Tech.
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Demonstrated experience with open-source computer vision and machine learning libraries (e.g., OpenCV, TensorFlow, and Caffe), Unix/Linux operating systems, and multi-threaded/parallel computing.
$123,900 - $230,400 a yearFull-timeExpandApply NowActive JobUpdated 6 days ago - UpvoteDownvoteShare Job
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Possess deep functional and technical understanding of the Machine Learning technologies (Google’s Cloud Platform, custom and COTS-embedded) and provide prescriptive guidance on how these are leveraged across the Fuse landscape.
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Lead and participate in multi-disciplinary, collaborative teams and contribute to the advancement of novel artificial intelligence and machine learning capabilities for today's warfighter.
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You’ll focus on machine learning architectural design, develop and review model and application code, and ensure high availability and performance of our machine learning applications.
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PhD in Computer Science, Operations Research, Statistics, or highly quantitative field (or equivalent experience) with strength in Deep Learning, Machine Learning, Data Mining, Statistical or other mathematical analysis.
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Experience in leading, building and supporting scalable and reliable data solutions, AI/machine learning powered systems that can enable fast prototyping and advanced analytics using modern big data and ML/AI technologies (Hadoop, Spark, Cloud, No-SQL, TensorFlow, H2O etc.
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We are looking for a passionate Machine Learning Data Science Research Engineer to design and implement deep learning solutions for NRL and graph construction/generation/completion.
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6+ years of proven experience in Data Science, predictive modeling using Statistics and Machine Learning. The team is part of Investment Risk Management Group and supports multiple investment risk managers (across Equity, Fixed Income and Alternatives) by developing data science solutions and machine learning techniques.
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Familiarity with machine learning frameworks (e.g., TensorFlow, PyTorch) and libraries (e.g., scikit-learn). ○ Experience in Machine Learning engineer or Infrastructure roles, with a focus on Machine Learning infrastructure.
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Experience with using GPUs for machine learning using frameworks, including PyTorch or TensorFlow. Experience with application areas of machine learning, including computer vision, natural language processing, and learning on graphs.
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Are you fascinated by the possibilities presented by machine learning and artifi cia l intelligence advances? You'll perform research in machine learning, including adversarial machine learning, algorithmic fa irn ess, and model interpretability.
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Mastery of Azure AI services beyond the basics, including Azure Machine Learning, Azure Cognitive Services, Azure Databricks, and Azure Synapse Analytics, would make you a valuable asset.
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Experience with IBM AI Fairness 360 (AIF360) - preference; Machine Learning (ML); Natural Language Processing (NLP); spaCy and NLTK, as well as language models like BERT and RoBERTa, are desired.
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