Sr. Distinguished Applied Researcher

About the role

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.

Responsibilities

• Partner with a cross-functional team of data scientists, software engineers, machine learning engineers, and product managers to deliver AI-powered platforms and solutions that change how customers interact with their money.
• 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.
• Leverage a broad stack of technologies — Pytorch, AWS Ultraclusters, Huggingface, Lightning, VectorDBs, and more — to reveal the insights hidden within huge volumes of numeric and textual data.
• Flex your interpersonal skills to translate the complexity of your work into tangible business goals.

Requirements

• 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. You stay current on published state-of-the-art methods, technologies, and applications and seek out opportunities to apply them.
• Creative. You thrive on bringing definition to big, undefined problems. You love 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. You’re 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. You have a deep understanding of the foundations of AI methodologies. You have expertise in one or more of the following: training optimization, self-supervised learning, robustness, explainability, RLHF.
• An engineering mindset as shown by a track record of delivering models at scale both in terms of training data and inference volumes. You have experience building large deep learning models, whether on language, images, events, or graphs, as well as expertise in one or more of the following: training optimization, self-supervised learning, robustness, explainability, RLHF.
• Experience in delivering libraries, platform level code or solution level code to existing products. You have a demonstrated ability to own and pursue a research agenda, including choosing impactful research problems and autonomously carrying out long-running projects.

Qualifications

• PhD in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields plus 6 years of experience in Applied Research or M.S. in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields plus 8 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 Masters with 10 years of industrial NLP research experience
• Core contributor to team that has trained a large language model from scratch (10B + parameters, 500B+ tokens)
• Multiple publications at ACL, NAACL and EMNLP, Neurips, ICML or ICLR 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)
• Worked on an LLM (open source or commercial) that is currently available for use
• Demonstrated ability to guide the technical direction of a large-scale model training team
• Experience working with 500+ node clusters of GPUs
• Worked on LLM scaled to 70B parameters and 1T+ tokens
• Experience with common training optimization frameworks (deep speed, nemo)

Benefits

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 for employment 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, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections 4901-4920; New York 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.