
location_onNYU Paulson Center, 181, Mercer Street, University Village, Manhattan, New York County, New York, 10012, United States
At Capital One, we are creating trustworthy and reliable AI systems to change banking for good. For years, we have led the industry in using machine learning to create real-time, intelligent, automated customer experiences. From informing customers about unusual charges to answering their questions instantly, our applications of AI and ML bring humanity and simplicity to banking. We are committed to building world-class applied science and engineering teams to reimagine how we serve the customers and businesses that love our products.
The AI Foundations team sits 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 collaborate with product, technology, and business leaders to apply the state of the art in AI to our business.
As a Sr. Distinguished Applied Researcher, you will drive strategic direction through collaboration with Applied Science, Engineering, and Product leaders across Capital One. This is an individual contributor role where you will guide and mentor a team of applied scientists and their managers without being a direct people leader. You will serve as an external leader representing Capital One in the research community, collaborating with prominent faculty members in the relevant AI research community.
You will partner with cross-functional teams to deliver AI-powered products that change how customers interact with their money. Your work involves building AI foundation models through all phases of development, from design through training, evaluation, validation, and implementation. You will engage in high-impact applied research to push the latest AI developments into the next generation of customer experiences, translating the complexity of your work into tangible business goals.
Candidates are expected to submit applications for a minimum of 5 business days. Please note that no agencies are accepted for this position. If you require an accommodation during the recruiting process, please contact Capital One Recruiting at 1-800-304-9102 or via email at RecruitingAccommodation@capitalone.com.
Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. We promote a drug-free workplace and consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws. We are committed to building a diverse and inclusive environment where talent can thrive.
Work model: On-site
NYU Paulson Center, 181, Mercer Street, University Village, Manhattan, New York County, New York, 10012, United States
New York, New York
PhD in Computer Science, Machine Learning, Computer Engineering, Applied Mathematics, or Electrical Engineering. LLM expertise including: PhD focus on NLP or Masters with 10 years of industrial NLP research experience; core contributor to a team that trained a large language model from scratch (10B+ parameters, 500B+ tokens); numerous publications at ACL, NAACL, EMNLP, Neurips, ICML, or ICLR on LLM pre-training topics; experience working on an available LLM (open source or commercial); demonstrated ability to guide the technical direction of a large-scale model training team; experience working with 500+ node GPU clusters; experience with LLMs scaled to 70B parameters and 1T+ tokens; experience with common training optimization frameworks such as DeepSpeed and NeMo.
Capital One • New York, New York
Capital One • New York, New York
Capital One • New York, New York
Skills: Machine Learning, Ai, Pytorch, Aws, Huggingface, Lightning, Vectordbs, Deep Learning, LLM, NLP.
Education: PhD in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics or related fields; M.S. in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics or related fields.