Quantitative Researcher

Overview Applied Researcher I – AI Foundations, Recommendation Systems, Personalization, Reinforcement Learning At Capital One, we are creating trustworthy and reliable AI systems, changing banking for good. We lead the industry by using machine learning to create real‑time, intelligent, automated customer experiences, from detecting unusual charges to answering questions instantly. This role focuses on applying emerging AI capabilities to transform how our customers and businesses interact with our products. Team Description The AI Foundations team is at the center of bringing our vision for AI to life. We partner with academia, product, technology, and business leaders to apply state‑of‑the‑art AI to real business problems and build production systems. Responsibilities 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, HuggingFace, Lightning, VectorDBs, and more—to reveal insights hidden within large 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 apply the latest AI developments and push them into next‑generation customer experiences. Translate the complexity of your work into tangible business goals, communicating results effectively to stakeholders. Ideal Candidate You love the process of analyzing and creating, yet share our passion for doing the right thing for customers. Innovative: you continually research and evaluate emerging technologies, stay current on state‑of‑the‑art methods, and identify opportunities to apply them. Creative: you thrive on bringing definition to big, undefined problems, asking questions, and finding answers without fear of sharing new ideas. A leader: you challenge conventional thinking, work with stakeholders to improve the status quo, and are passionate about talent development for your team and beyond. Technical: you are comfortable with open‑source languages, have hands‑on experience developing AI foundation models and solutions on open‑source tools and cloud platforms, and possess a deep understanding of AI methodologies. Experience building large deep learning models—whether on language, images, events, or graphs—and expertise in at least one of the following: training optimization, self‑supervised learning, robustness, explainability, or RLHF. Engineering mindset: you have a track record of delivering models at scale (both training data and inference volumes) and of delivering libraries, platform‑level code, or solution‑level code to existing products. High‑quality research contributions: you have a record of first‑author publications or projects that demonstrate your ability to generate high‑quality ideas and improve existing ones in machine learning. Research agenda ownership: you can identify impactful problems, set a research agenda, and autonomously execute long‑running projects with results that drive business value. Basic Qualifications Currently has, or is in the process of obtaining, a PhD in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or a related field, with the required degree obtained on or before the scheduled start date. Or an MS in a related field plus at least 2 years of experience in applied research. Preferred Qualifications PhD in Computer Science, Machine Learning, Computer Engineering, Applied Mathematics, Electrical Engineering, or related field. LLM PhD with a focus on NLP, or a Master’s 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, SSL techniques, pre‑training optimization). Member of 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. Finetuning PhD focused on guiding LLMs with supervised finetuning, instruction‑tuning, dialogue finetuning, or parameter tuning. Demonstrated knowledge of transfer learning, model adaptation, and model guidance principles. Experience deploying a finetuned large language model. Data preparation publications studying tokenization, data quality, dataset curation, or labeling. Contribution to a major open‑source corpus. Contribution to open‑source libraries for data quality, dataset curation, or labeling. Benefits Capital One offers a comprehensive, competitive, and inclusive set of health, financial, and other benefits that support your total well‑being. Full details can be found on the Capital One Careers website. Immigration Sponsorship Capital One will consider sponsoring a new qualified applicant for employment authorization for this position. Equal Opportunity Employer 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 qualified applicants with a criminal history in accordance with applicable laws. #J-18808-Ljbffr