Capital One
Overview
Distinguished Applied Researcher At Capital One, we are creating trustworthy and reliable AI systems, changing banking for good. For years, Capital One has been leading the industry in using machine learning to create real-time, intelligent, automated customer experiences. From informing customers about unusual charges to answering their questions in real time, our applications of AI & ML are bringing humanity and simplicity to banking. We are committed to building world-class applied science and engineering teams and continue our industry leading capabilities with breakthrough product experiences and scalable, high-performance AI infrastructure. At Capital One, you will help bring the transformative power of emerging AI capabilities to reimagine how we serve our customers and businesses who have come to love the products and services we build. Team Description: The AI Foundations team is at the center of bringing our vision for AI at Capital One to life. Our work touches every aspect of the research life cycle, from partnering with Academia to building production systems. We work with product, technology and business leaders to apply the state of the art in AI to our business. 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. 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. In this role, you will: Partner with a cross-functional team of data scientists, software engineers, machine learning engineers and product managers to deliver AI-powered products that change how customers interact with their money. Leverage a broad stack of technologies - PyTorch, AWS Ultraclusters, Hugging Face, Lightning, VectorDBs, and more - to reveal the insights hidden within huge volumes of numeric and textual data. Build AI foundation models through all phases of development, from design through training, evaluation, validation, and implementation. Engage in high impact applied research to take the latest AI developments and push them into the next generation of customer experiences. Translate the complexity of your work into tangible business goals through strong interpersonal communication. The Ideal Candidate: You love the process of analyzing and creating, and you are passionate about making the right decision for our customers. Innovative. You continually research and evaluate emerging technologies and stay current on state-of-the-art methods, technologies, and applications, applying them where appropriate. Creative. You thrive on defining large, undefined problems, asking questions, and sharing new ideas. Leader. You challenge conventional thinking and collaborate with stakeholders to improve the status quo and mentor talent. Technical. You are comfortable with open-source languages and have hands-on experience developing AI foundation models using open-source tools and cloud platforms. Have a deep understanding of AI foundations and methodologies. Experience building large deep learning models across language, image, event, or graph domains, with expertise in training optimization, self-supervised learning, robustness, explainability, RLHF. Engineering mindset with a track record of delivering models at scale (training data and inference volumes). Experience delivering libraries, platform-level code, or solution-level code for existing products. A track record of new ideas or improvements in machine learning, evidenced by first-author publications or significant projects. Ability to own and pursue a research agenda, including choosing impactful problems and autonomously carrying out long-running projects. Key Responsibilities: Partner with a cross-functional team of scientists, machine learning engineers, software engineers, and product managers to deliver AI-powered platforms and solutions that change how customers interact with their money. Build AI foundation models through all phases of development, from design through training, evaluation, validation, and implementation. Engage in high impact applied research to take the latest AI developments and push them into the next generation of customer experiences. Leverage a broad stack of technologies - PyTorch, AWS Ultraclusters, Hugging Face, Lightning, VectorDBs, and more - to reveal insights from large volumes of numeric and textual data. Translate the complexity of your work into tangible business goals with strong communication skills. Basic Qualifications: PhD in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related field with 4 years of applicable experience; or MS in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related field with 6 years of applicable experience. Preferred Qualifications: PhD in Computer Science, Machine Learning, Computer Engineering, Applied Mathematics, Electrical Engineering or related field. LLM experience. PhD focus on NLP or Masters with 10 years of industrial NLP research experience. Core contributor to a team that has trained a large language model from scratch (10B+ parameters, 500B+ tokens) or through continued pre-training, post-training pipelines for alignment and reasoning, LLM optimizations, and complex reasoning with multi-agent LLMs. Publications at ACL, NAACL, EMNLP, NeurIPS, ICML, or ICLR on topics related to pre-training of large language models. Experience with an LLM currently available for use (open source or commercial). Ability to guide the technical direction of a large-scale model training team. Experience with common training optimization frameworks (DeepSpeed, NeMo). Experience contributing to teams that have trained large language models from scratch or via continued pre-training and post-training pipelines. Capital One will consider sponsoring a new qualified applicant for employment authorization for this position. The minimum and maximum full-time annual salaries for this role are listed below by location. Salaries for part-time roles will be prorated based on hours. This role is also eligible for performance-based incentives, including cash bonuses and long-term incentives. Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits. Eligibility varies by status. For accommodations in applying, contact Recruiting. Capital One is an equal opportunity employer (EOE, including disability/vet) and promotes a drug-free workplace. Capital One will consider qualified applicants with criminal histories in a manner consistent with applicable laws. For accommodations or recruiting questions, contact Recruiting at 1-800-304-9102 or Careers@capitalone.com. Capital One does not endorse third-party products or information sourced from outside this site. This position posting may be for Capital One entities in different regions as noted in the posting.
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Distinguished Applied Researcher At Capital One, we are creating trustworthy and reliable AI systems, changing banking for good. For years, Capital One has been leading the industry in using machine learning to create real-time, intelligent, automated customer experiences. From informing customers about unusual charges to answering their questions in real time, our applications of AI & ML are bringing humanity and simplicity to banking. We are committed to building world-class applied science and engineering teams and continue our industry leading capabilities with breakthrough product experiences and scalable, high-performance AI infrastructure. At Capital One, you will help bring the transformative power of emerging AI capabilities to reimagine how we serve our customers and businesses who have come to love the products and services we build. Team Description: The AI Foundations team is at the center of bringing our vision for AI at Capital One to life. Our work touches every aspect of the research life cycle, from partnering with Academia to building production systems. We work with product, technology and business leaders to apply the state of the art in AI to our business. 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. 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. In this role, you will: Partner with a cross-functional team of data scientists, software engineers, machine learning engineers and product managers to deliver AI-powered products that change how customers interact with their money. Leverage a broad stack of technologies - PyTorch, AWS Ultraclusters, Hugging Face, Lightning, VectorDBs, and more - to reveal the insights hidden within huge volumes of numeric and textual data. Build AI foundation models through all phases of development, from design through training, evaluation, validation, and implementation. Engage in high impact applied research to take the latest AI developments and push them into the next generation of customer experiences. Translate the complexity of your work into tangible business goals through strong interpersonal communication. The Ideal Candidate: You love the process of analyzing and creating, and you are passionate about making the right decision for our customers. Innovative. You continually research and evaluate emerging technologies and stay current on state-of-the-art methods, technologies, and applications, applying them where appropriate. Creative. You thrive on defining large, undefined problems, asking questions, and sharing new ideas. Leader. You challenge conventional thinking and collaborate with stakeholders to improve the status quo and mentor talent. Technical. You are comfortable with open-source languages and have hands-on experience developing AI foundation models using open-source tools and cloud platforms. Have a deep understanding of AI foundations and methodologies. Experience building large deep learning models across language, image, event, or graph domains, with expertise in training optimization, self-supervised learning, robustness, explainability, RLHF. Engineering mindset with a track record of delivering models at scale (training data and inference volumes). Experience delivering libraries, platform-level code, or solution-level code for existing products. A track record of new ideas or improvements in machine learning, evidenced by first-author publications or significant projects. Ability to own and pursue a research agenda, including choosing impactful problems and autonomously carrying out long-running projects. Key Responsibilities: Partner with a cross-functional team of scientists, machine learning engineers, software engineers, and product managers to deliver AI-powered platforms and solutions that change how customers interact with their money. Build AI foundation models through all phases of development, from design through training, evaluation, validation, and implementation. Engage in high impact applied research to take the latest AI developments and push them into the next generation of customer experiences. Leverage a broad stack of technologies - PyTorch, AWS Ultraclusters, Hugging Face, Lightning, VectorDBs, and more - to reveal insights from large volumes of numeric and textual data. Translate the complexity of your work into tangible business goals with strong communication skills. Basic Qualifications: PhD in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related field with 4 years of applicable experience; or MS in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related field with 6 years of applicable experience. Preferred Qualifications: PhD in Computer Science, Machine Learning, Computer Engineering, Applied Mathematics, Electrical Engineering or related field. LLM experience. PhD focus on NLP or Masters with 10 years of industrial NLP research experience. Core contributor to a team that has trained a large language model from scratch (10B+ parameters, 500B+ tokens) or through continued pre-training, post-training pipelines for alignment and reasoning, LLM optimizations, and complex reasoning with multi-agent LLMs. Publications at ACL, NAACL, EMNLP, NeurIPS, ICML, or ICLR on topics related to pre-training of large language models. Experience with an LLM currently available for use (open source or commercial). Ability to guide the technical direction of a large-scale model training team. Experience with common training optimization frameworks (DeepSpeed, NeMo). Experience contributing to teams that have trained large language models from scratch or via continued pre-training and post-training pipelines. Capital One will consider sponsoring a new qualified applicant for employment authorization for this position. The minimum and maximum full-time annual salaries for this role are listed below by location. Salaries for part-time roles will be prorated based on hours. This role is also eligible for performance-based incentives, including cash bonuses and long-term incentives. Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits. Eligibility varies by status. For accommodations in applying, contact Recruiting. Capital One is an equal opportunity employer (EOE, including disability/vet) and promotes a drug-free workplace. Capital One will consider qualified applicants with criminal histories in a manner consistent with applicable laws. For accommodations or recruiting questions, contact Recruiting at 1-800-304-9102 or Careers@capitalone.com. Capital One does not endorse third-party products or information sourced from outside this site. This position posting may be for Capital One entities in different regions as noted in the posting.
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