Applied Scientist

Sr. Distinguished Applied Researcher Overview: At Capital One, we are creating trustworthy and reliable AI systems, changing banking for good. For years, Capital One has 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 and continue our industry leading capabilities with breakthrough product experiences and scalable, high‑performance AI infrastructure. Team Description 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 work with product, technology and business leaders to apply the state of the art in AI to our business. In this role you will Partner with cross‑functional teams 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—including Pytorch, AWS Ultraclusters, Huggingface, Lightning, VectorDBs, and more—to uncover insights locked in large volumes of numeric and textual data. Build AI foundation models through all phases of development, from design to training, evaluation, validation, and implementation. Engage in high‑impact applied research to advance the latest AI developments into next‑generation customer experiences. Translate the complexity of your work into tangible business goals and communicate results to stakeholders. The Ideal Candidate Passion for analysis and creation, with a focus on doing the right thing for customers. Innovative, continually researching emerging technologies and applying them to solve problems. Creative, able to turn undefined problems into clear solutions and comfortable sharing new ideas. Leader, challenging conventional thinking and fostering talent development beyond the immediate team. Technical, hands‑on experience developing AI foundation models and solutions using open‑source tools and cloud platforms. Deep understanding of foundational AI methodologies and experience building large deep‑learning models for language, images, events or graphs. Expertise in training optimization, self‑supervised learning, robustness, explainability or RLHF. Engineering mindset, proven record of delivering scalable models in training data and inference volume. Track record of publishing as first author or leading impactful machine‑learning projects. A professional capable of owning and pursuing a long‑term research agenda autonomously. Key Responsibilities Partner with cross‑functional teams to deliver AI‑powered platforms and solutions for customer financial interactions. Build AI foundation models through design, training, evaluation, validation, and deployment. Lead high‑impact applied research to embed cutting‑edge AI into customer experiences. Utilize a broad technology stack—including Pytorch, AWS Ultraclusters, Huggingface, Lightning, VectorDBs—to analyze large numeric and textual datasets. Translate technical insights into business goals for stakeholder alignment. Basic Qualifications PhD in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics or related field plus 6 years of applied research experience, or MS in the same fields plus 8 years of applied research experience. Preferred Qualifications PhD in Computer Science, Machine Learning, Computer Engineering, Applied Mathematics, Electrical Engineering or related field. LLM research experience, including: PhD focus on NLP or Master’s with 10 years of industrial NLP research. Core contributor to a team that trained a large language model from scratch (10B+ parameters, 500B+ tokens). Numerous publications at top venues (ACL, NAACL, EMNLP, NeurIPS, ICML, ICLR) on large‑language‑model pre‑training, SSL techniques, or model optimization. Experience with an LLM (open‑source or commercial) that is publicly available. Led a large‑scale model training team. Experience working with 500+ node GPU clusters, up to 70B parameters and 1T+ tokens. Familiarity with training optimization frameworks such as DeepSpeed or NeMo. Equal Opportunity Employer Capital One is an equal opportunity employer (EOE, including disability/veteran) 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 a manner consistent with applicable laws. Compensation & Benefits Salary ranges (minimum–maximum) by location are listed below. This role is also eligible for performance‑based incentive compensation. Capital One offers a comprehensive benefit package; eligibility varies based on status. Locations and pay ranges: Sales Territory: $311,900 – $356,000; Cambridge, MA: $343,100 – $391,600; McLean, VA: $343,100 – $391,600; New York, NY: $374,300 – $427,200; San Francisco, CA: $374,300 – $427,200. Application Process This role is open for a minimum of 5 business days. No agencies. Contact For accommodations, contact View email address on click.appcast.io. For technical support or questions about the recruiting process, email View email address on click.appcast.io. Legal Notice Capital One does not provide, endorse, or guarantee third‑party products or services. This posting is for Capital One Financial. #J-18808-Ljbffr