Job Description

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. At Capital One, you will help bring the transformative power of emerging AI capabilities to reimagine how we serve our customers and businesses who have come to love the products and services we build. 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. Role 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, Hugging Face, Lightning, vector‑DBs, and more – to reveal the insights hidden within huge 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 take the latest AI developments and push them into the next generation of customer experiences. Translate the complexity of your work into tangible business goals through strong interpersonal skills. The Ideal Candidate You love the process of analyzing and creating, but also share our passion to do the right thing. You know at the end of the day it’s about making the right decision for our customers. Innovative. You continually research and evaluate emerging technologies, staying current on state‑of‑the‑art methods and applications. Creative. You thrive on bringing definition to big, undefined problems, asking questions, and pushing hard to find answers. You’re not afraid to share a new idea. A leader. You challenge conventional thinking and work with stakeholders to identify and improve the status quo, and you are passionate about talent development for your own team and beyond. Technical. You’re comfortable with open‑source languages and are passionate about developing further. You have hands‑on experience developing AI foundation models and solutions using open‑source tools and cloud computing platforms. Deep understanding of the foundations of AI methodologies. Experience building large deep‑learning models, whether on language, images, events, or graphs, with expertise in training optimization, self‑supervised learning, robustness, explainability, or RLHF. Engineering mindset with a track record of delivering models at scale in both training data and inference volumes. Experience delivering libraries, platform‑level code, or solution‑level code to existing products. Track record of new ideas or improving existing ideas in machine learning, demonstrated by accomplishments such as first‑author publications or projects. Ability to own and pursue a research agenda, choosing impactful problems and independently carrying out long‑running projects. Basic Qualifications Currently has, or is in the process of obtaining, a PhD in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields, or an M.S. in the same fields with 4–6 years of experience in applied research. Preferred Qualifications PhD in Computer Science, Machine Learning, Computer Engineering, Applied Mathematics, Electrical Engineering or related fields. LLM PhD focus on NLP or M.Sc. with 5 years of industrial NLP research experience. Multiple publications on large‑language‑model pre‑training or SSL techniques. Member of a team that has trained a large language model from scratch (10B+ parameters, 500B+ tokens). Publications in deep‑learning theory and top conferences such as ACL, NAACL, EMNLP, NeurIPS, ICML, ICLR. Optimization (Training & Inference) PhD focused on optimizing training of very large deep‑learning models. Experience and publications on model sparsification, quantization, training parallelism, gradient checkpointing, compression, or compiler design for 10B+ models. 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. Experience deploying a fine‑tuned large language model. Capital One will consider sponsoring a new qualified applicant for employment authorization for this position. Compensation and Benefits Cambridge, MA: $262,500 – $299,600 for Applied ResearcherII McLean, VA: $262,500 – $299,600 for Applied ResearcherII NewYork, NY: $286,400 – $326,800 for Applied ResearcherII SanJose, CA: $286,400 – $326,800 for Applied ResearcherII Capital One offers a comprehensive, competitive, and inclusive set of health, financial, and other benefits that support your total well‑being. Eligibility varies based on full or part‑time status, exempt or non‑exempt status, and management level. Legal Statements Capital One is an equal‑opportunity employer (EOE, including disability/vet) committed to non‑discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug‑free workplace. Capital One will consider qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article23‑A of the NewYork Correction Law; SanFrancisco, California Police Code Article49, Sections4901‑4920; NewYork City’s Fair Chance Act; Philadelphia’s Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries. #J-18808-Ljbffr

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