Applied Researcher I (AI Foundations, LLM Core and Agentic AI)
Not Disclosed•Full-TimeOn-site
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, changing banking for good. For years, we have 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 to continue our industry-leading capabilities with breakthrough product experiences and scalable, high-performance AI infrastructure.
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 collaborate closely with product, technology, and business leaders to apply the state of the art in AI to our business, helping to reimagine how we serve the customers and businesses who have come to love the products and services we build.
As an Applied Researcher, you will partner with a cross-functional team of data scientists, software engineers, and product managers to deliver AI-powered products that change how customers interact with their money. You will leverage a broad stack of technologies to reveal insights hidden within huge volumes of numeric and textual data, building AI foundation models through all phases of development from design through training, evaluation, validation, and implementation.
This role is designed for those who love the process of analyzing and creating, while sharing a passion for doing the right thing for our customers. You will engage in high-impact applied research to push the latest AI developments into the next generation of customer experiences. Success in this role requires the ability to flex interpersonal skills to translate the complexity of your work into tangible business goals, challenge conventional thinking, and own a research agenda that includes choosing impactful problems and autonomously carrying out long-running projects.
Candidates hired to work in other locations will be subject to the pay range associated with that location. The actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate's offer letter. This role is expected to accept applications for a minimum of 5 business days.
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 will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws. If you require an accommodation to apply, please contact Capital One Recruiting at 1-800-304-9102 or via email at RecruitingAccommodation@capitalone.com.
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. PhD focus on NLP or Masters with 5 years of industrial NLP research experience. Multiple publications on topics related to the pre-training of large language models (e.g., technical reports of pre-trained LLMs, SSL techniques, model pre-training optimization). Membership on a team that has trained a large language model from scratch (10B+ parameters, 500B+ tokens). Publications in deep learning theory. Publications at ACL, NAACL, EMNLP, Neurips, ICML, or ICLR. PhD focused on topics related to optimizing training of very large deep learning models. Multiple years of experience and/or publications on Model Sparsification, Quantization, Training Parallelism/Partitioning Design, Gradient Checkpointing, or Model Compression. Experience optimizing training for a 10B+ model. Deep knowledge of deep learning algorithmic and/or optimizer design. Experience with compiler design. PhD focused on topics related to guiding LLMs with further tasks (Supervised Finetuning, Instruction-Tuning, Dialogue-Finetuning, Parameter Tuning). Demonstrated knowledge of principles of transfer learning, model adaptation, and model guidance. Experience deploying a fine-tuned large language model.
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, LLM, NLP, Deep Learning.
Education: PhD in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields required; Master's in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields with 2 years of experience; PhD in Computer Science, Machine Learning, Computer Engineering, Applied Mathematics, Electrical Engineering or related fields preferred.