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2+ years of experience with OpenSearch, Kafka, Vue.js and some exposure to Machine Learning. Utilize programming languages like JavaScript, Java, HTML/CSS, TypeScript, SQL, Python, and Go, Open Source RDBMS and NoSQL databases, Container Orchestration services including Docker and Kubernetes, and a variety of AWS tools and services.
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As a Capital One Lead Software Engineer, you'll have the opportunity to be on the forefront of driving a major transformation within Capital One. At Capital One, you'll be part of a big group of makers, breakers, doers and disruptors, who solve real problems and meet real customer needs.
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Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC.
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Capital One is an equal opportunity employer committed to diversity and inclusion in the workplace. At this time, Capital One will not sponsor a new applicant for employment authorization for this position.
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Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site. Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being.
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The Machine Learning Experience (MLX) team supports an internally hosted Model Training ecosystem running critical infrastructure for machine learning and data analysis across Capital One. You will get an opportunity to collaborate on the 1000's of models being trained on the platform by 4000+ users and dive deep into the unique engineering challenges we have around compute, data access, security and scale.
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For years, Capital One has been leading the industry in using machine learning to create real-time, intelligent, automated customer experiences. Leading contributions to one or more large open source corpus (1 Trillion + tokens.
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As a Data Scientist at Capital One, you'll be part of a team that's leading the next wave of disruption at a whole new scale, using the latest in computing and machine learning technologies and operating across billions of customer records to unlock the big opportunities that help everyday people save money, time and agony in their financial lives.
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PhD in Computer Science, Machine Learning, Computer Engineering, Applied Mathematics, Electrical Engineering or related fields. This is a people manager role that will lead teams to drive strategic direction through collaboration with Applied Science, Engineering and Product leaders across Capital One. As a well-respected people leader, you will guide and mentor a team of applied scientists.
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Has worked on an LLM (open source or commercial) that is currently available for use. Experience building large deep learning models, whether on language, images, events, or graphs, as well as expertise in one or more of the following: training optimization, self-supervised learning, robustness, explainability, RLHF.
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This is an individual contributor (IC) role driving strategic direction through collaboration with Applied Science, Engineering and Product leaders across Capital One. As a well-respected IC leader, you will guide and mentor a team of applied scientists and their managers without being a direct people leader.
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You have hands-on experience developing AI foundation models and solutions using open-source tools and cloud computing platforms. Core contributor to open source libraries for data quality, dataset curation, or labeling.
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The Card Fraud Data Science team builds the machine learning models that help protect our customers and Capital One against fraudsters. You have hands-on experience developing data science solutions using open-source tools and cloud computing platforms.
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At Capital One, we are creating trustworthy and reliable AI systems, changing banking for good. You will be expected to be an external leader representing Capital One in the research community, collaborating with prominent faculty members in the relevant AI research community.
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