Capital One
Applied Researcher I (AI Foundations)
Capital One, San Francisco, California, United States, 94199
Overview
Applied Researcher I (AI Foundations) at Capital One. We are building trustworthy and reliable AI systems to reimagine banking experiences and deliver scalable, high-performance AI infrastructure. This role helps bring emerging AI capabilities to customers and businesses who rely on Capital One products and services. Team Description
The AI Foundations team centers AI at Capital One’s vision, partnering with academia and building production systems. We collaborate with product, technology and business leaders to apply state-of-the-art AI to our business. Responsibilities
Partner with a cross-functional team of data scientists, software engineers, ML engineers and product managers to deliver AI-powered products that change how customers interact with their money. Leverage technologies including PyTorch, AWS Ultraclusters, Huggingface, Lightning, VectorDBs, and more to extract insights from large volumes of numeric and textual data. Build AI foundation models through all development phases: design, training, evaluation, validation, and implementation. Engage in high-impact applied research to push AI developments into next-generation customer experiences. Translate complex work into tangible business goals through strong interpersonal communication. Ideal Candidate
Analytical and creative with a focus on making decisions that benefit customers; integrity is valued. Innovative and current with emerging technologies, publications, and applications, with a willingness to apply them. Creative problem-solver who questions assumptions and shares new ideas. Leader who challenges the status quo and advocates for talent development. Technical proficiency with open-source tools and cloud platforms; experience building AI foundation models. Deep understanding of AI methodologies and experience building large deep learning models, with expertise in training optimization, self-supervised learning, robustness, explainability, or RLHF. Engineering mindset with a track record of delivering models at scale (training data and inference) and contributing to libraries or platform-level code. Proven ability to own a research agenda and carry out long-running projects autonomously. Basic Qualifications
Currently pursuing or holding a PhD in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields with an expected degree date on/before start; or MS in related fields plus 2 years of applied research experience. Preferred Qualifications
PhD in Computer Science, ML, Computer Engineering, Applied Mathematics, Electrical Engineering or related fields Experience and focus areas in LLMs, including NLP and pre-training, with multiple publications on related topics; or experience training a large language model from scratch (10B+ parameters, 500B+ tokens). Publications in deep learning theory and venues such as ACL, NAACL, EMNLP, NeurIPS, ICML or ICLR. Expertise in Behavioral Models (graph neural networks, sequential models, multivariate time series) and related scalable graph/model training topics; production real-time and streaming systems; open source contributions (e.g., PyTorch Geometric, DGL). Optimization, training and inference experience for very large models; familiarity with model sparsification, quantization, training parallelism, gradient checkpointing, model compression, compiler design. Finetuning expertise including supervised fine-tuning, instruction-tuning, dialogue-finetuning, and transfer learning; ability to deploy fine-tuned models. Data preparation focus, including tokenization, data quality, dataset curation or labeling; contributions to major open source corpora or libraries for data quality or labeling. Note:
Capital One will consider sponsoring qualified applicants for employment authorization. The salary ranges below reflect location-based pay for full-time roles and may vary by location. Salaries for other locations may differ; final offer details will be provided in the offer letter. Salary ranges by location (Applied Researcher I): Cambridge, MA: $214,500 - $244,800; New York, NY: $234,000 - $267,000; San Francisco, CA: $234,000 - $267,000; San Jose, CA: $234,000 - $267,000. Other locations follow the applicable local pay range. This role may be eligible for performance-based incentives, including cash bonuses or long-term incentives. Capital One offers comprehensive health, financial and other benefits. Eligibility varies by status. For accommodations in the recruiting process or for questions about applying, contact Capital One Recruiting at 1-800-304-9102 or via email at RecruitingAccommodation@capitalone.com. Capital One is an equal opportunity employer and a drug-free workplace. We consider qualified applicants with criminal histories in a manner consistent with applicable laws and regulations. For more information or to apply, visit Capital One Careers. This role is open to candidates who meet the requirements and can start by the scheduled start date.
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Applied Researcher I (AI Foundations) at Capital One. We are building trustworthy and reliable AI systems to reimagine banking experiences and deliver scalable, high-performance AI infrastructure. This role helps bring emerging AI capabilities to customers and businesses who rely on Capital One products and services. Team Description
The AI Foundations team centers AI at Capital One’s vision, partnering with academia and building production systems. We collaborate with product, technology and business leaders to apply state-of-the-art AI to our business. Responsibilities
Partner with a cross-functional team of data scientists, software engineers, ML engineers and product managers to deliver AI-powered products that change how customers interact with their money. Leverage technologies including PyTorch, AWS Ultraclusters, Huggingface, Lightning, VectorDBs, and more to extract insights from large volumes of numeric and textual data. Build AI foundation models through all development phases: design, training, evaluation, validation, and implementation. Engage in high-impact applied research to push AI developments into next-generation customer experiences. Translate complex work into tangible business goals through strong interpersonal communication. Ideal Candidate
Analytical and creative with a focus on making decisions that benefit customers; integrity is valued. Innovative and current with emerging technologies, publications, and applications, with a willingness to apply them. Creative problem-solver who questions assumptions and shares new ideas. Leader who challenges the status quo and advocates for talent development. Technical proficiency with open-source tools and cloud platforms; experience building AI foundation models. Deep understanding of AI methodologies and experience building large deep learning models, with expertise in training optimization, self-supervised learning, robustness, explainability, or RLHF. Engineering mindset with a track record of delivering models at scale (training data and inference) and contributing to libraries or platform-level code. Proven ability to own a research agenda and carry out long-running projects autonomously. Basic Qualifications
Currently pursuing or holding a PhD in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields with an expected degree date on/before start; or MS in related fields plus 2 years of applied research experience. Preferred Qualifications
PhD in Computer Science, ML, Computer Engineering, Applied Mathematics, Electrical Engineering or related fields Experience and focus areas in LLMs, including NLP and pre-training, with multiple publications on related topics; or experience training a large language model from scratch (10B+ parameters, 500B+ tokens). Publications in deep learning theory and venues such as ACL, NAACL, EMNLP, NeurIPS, ICML or ICLR. Expertise in Behavioral Models (graph neural networks, sequential models, multivariate time series) and related scalable graph/model training topics; production real-time and streaming systems; open source contributions (e.g., PyTorch Geometric, DGL). Optimization, training and inference experience for very large models; familiarity with model sparsification, quantization, training parallelism, gradient checkpointing, model compression, compiler design. Finetuning expertise including supervised fine-tuning, instruction-tuning, dialogue-finetuning, and transfer learning; ability to deploy fine-tuned models. Data preparation focus, including tokenization, data quality, dataset curation or labeling; contributions to major open source corpora or libraries for data quality or labeling. Note:
Capital One will consider sponsoring qualified applicants for employment authorization. The salary ranges below reflect location-based pay for full-time roles and may vary by location. Salaries for other locations may differ; final offer details will be provided in the offer letter. Salary ranges by location (Applied Researcher I): Cambridge, MA: $214,500 - $244,800; New York, NY: $234,000 - $267,000; San Francisco, CA: $234,000 - $267,000; San Jose, CA: $234,000 - $267,000. Other locations follow the applicable local pay range. This role may be eligible for performance-based incentives, including cash bonuses or long-term incentives. Capital One offers comprehensive health, financial and other benefits. Eligibility varies by status. For accommodations in the recruiting process or for questions about applying, contact Capital One Recruiting at 1-800-304-9102 or via email at RecruitingAccommodation@capitalone.com. Capital One is an equal opportunity employer and a drug-free workplace. We consider qualified applicants with criminal histories in a manner consistent with applicable laws and regulations. For more information or to apply, visit Capital One Careers. This role is open to candidates who meet the requirements and can start by the scheduled start date.
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