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, ensuring that the transformative power of emerging AI capabilities reimagines how we serve our customers and the businesses that rely on our products.
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 for those who love the process of analyzing and creating, sharing a passion to do the right thing and make the right decision 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 an engineering mindset with a track record of delivering models at scale, the ability to own and pursue a research agenda, and the interpersonal skills to translate the complexity of your work into tangible business goals.
Capital One is committed to a fair and transparent hiring process. We expect applications to be accepted for a minimum of 5 business days. For candidates requiring an accommodation during the recruiting process, please contact Capital One Recruiting at 1-800-304-9102 or via email at RecruitingAccommodation@capitalone.com. All information provided will be kept confidential and used only to the extent required to provide needed reasonable accommodations.
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 foster an inclusive culture where we challenge conventional thinking and work with stakeholders to identify and improve the status quo.
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 focus on NLP or Masters with 5 years of industrial NLP research experience. Multiple publications on pre-training of large language models (e.g., technical reports, SSL techniques, model pre-training optimization). Membership in 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 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 guiding LLMs with further tasks (Supervised Finetuning, Instruction-Tuning, Dialogue-Finetuning, Parameter Tuning). Demonstrated knowledge of transfer learning, model adaptation, and model guidance principles. 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, Deep Learning, LLM, NLP.
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.