Staff Applied Researcher

About the role

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.

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. This is an individual contributor (IC) role driving strategic direction through collaboration with Applied Science, Engineering and Product leaders across Capital One. As a well-respected IC leader, you will guide and mentor a team of applied scientists and their managers without being a direct people leader. You will be expected to be an external leader representing Capital One in the research community, collaborating with prominent faculty members in the relevant AI research community.

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, VectorDBs, 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.
• Flex interpersonal skills to translate the complexity of your work into tangible business goals.

Qualifications

• Basic Qualifications:
  • PhD in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields plus 4 years of experience in Applied Research
  • or
  • M.S. in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields plus 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 focus
  • 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) or through continued pre-training, post-training pipeline for alignment and reasoning, LLM optimizations, complex reasoning with multi-agentic LLMs
  • Numerous 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)
  • Has 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 with common training optimization frameworks (DeepSpeed, NeMo)
  • Experience contributing to the team that has trained a large language model from scratch (10B+ parameters, 500B+ tokens) or through continued pre-training, post-training pipeline for alignment and reasoning, LLM optimizations, complex reasoning with multi-agentic LLMs

Skills

• 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 (language, images, events, or graphs)
• Expertise in one or more of the following: training optimization, self-supervised learning, robustness, explainability, RLHF
• Engineering mindset with a track record of delivering models at scale (training data and inference volumes)
• Experience delivering libraries, platform-level code, or solution-level code to existing products
• Track record of coming up with new ideas or improving upon existing ideas in machine learning, demonstrated by first-author publications or projects
• Ability to own and pursue a research agenda, including choosing impactful research problems and autonomously carrying out long-running projects

Pay

Capital One will consider sponsoring a new qualified applicant for employment authorization for this position.

• Sales Territory: $278,400 – $317,700
• Cambridge, MA: $306,300 – $349,500
• McLean, VA: $306,300 – $349,500
• New York, NY: $334,100 – $381,300
• San Francisco, CA: $334,100 – $381,300

Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate’s offer letter. This role is also eligible to earn performance-based incentive compensation, which may include cash bonus(es) and/or long-term incentives (LTI). Incentives could be discretionary or non-discretionary depending on the plan.