Job Offer: Applied Researcher I at Capital One

Location: 11 West 19th Street (22008), New York, NY, United States

Overview

At Capital One, we are revolutionizing the banking industry with trustworthy and reliable AI systems. For years, we have been at the forefront of using machine learning to create real-time, intelligent, and automated customer experiences. Our innovative AI & ML applications help inform customers about unusual charges and answer their questions in real time, making banking simpler and more human-centric. Join us in building world-class applied science and engineering teams to enhance our industry-leading capabilities with breakthrough product experiences and scalable, high-performance AI infrastructure.

Team Description

The AI Foundations team is the core driver of our AI vision at Capital One. We engage in every aspect of the research life cycle, from academia partnerships to building production systems. Collaborating with product, technology, and business leaders, we apply cutting-edge AI to transform our business operations.

Role Description

As an Applied Researcher I, you will:

• Partner with a cross-functional team of data scientists, software engineers, machine learning engineers, and product managers to develop AI-powered products that transform customer interactions.
• Utilize a broad stack of technologies—Pytorch, AWS Ultraclusters, Huggingface, Lightning, VectorDBs, and more—to unlock insights from vast numeric and textual data.
• Build AI foundation models through design, training, evaluation, validation, and implementation phases.
• Conduct high-impact applied research to integrate the latest AI developments into next-generation customer experiences.
• Translate the complexity of your work into tangible business goals using your interpersonal skills.

Ideal Candidate

We are looking for someone who:

• Is passionate about making the right decisions for our customers and enjoys the process of analyzing and creating.
• Constantly researches and evaluates emerging technologies and stays current on the latest methods and applications.
• Is creative and enjoys defining and solving big, undefined problems.
• Demonstrates leadership qualities and challenges conventional thinking to improve the status quo.
• Is technically skilled and comfortable with open-source languages, with hands-on experience in developing AI foundation models and solutions using cloud computing platforms.
• Has a deep understanding of AI methodologies and experience in building large deep learning models dealing with language, images, events, or graphs.
• Shows an engineering mindset by delivering models at scale, both in terms of training data and inference volumes.
• Has experience delivering libraries, platform-level code, or solution-level code to existing products.
• Has a track record of high-quality ideas or improvements in machine learning, demonstrated through first-author publications or projects.
• Can autonomously manage a research agenda and carry out long-running projects.

Qualifications

Basic Qualifications

• Currently holds or is in the process of obtaining, a PhD, with the expectation that the degree will be obtained before the scheduled start date, or an M.S. with 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 fields.
• Experience in guiding LLMs with tasks such as Supervised Finetuning, Instruction-Tuning, Dialogue-Finetuning, Parameter Tuning.
• Knowledge of transfer learning principles, model adaptation, and model guidance.
• Experience deploying fine-tuned large language models.

Compensation

The full-time annual salary for this role by location is as follows:

• New York City (Hybrid On-Site): $230,000 - $262,500
• San Francisco, California (Hybrid On-Site): $243,700 - $278,100

Salaries for part-time roles will be prorated based on the agreed-upon number of work hours. This role is eligible for performance-based incentives, including cash bonuses and/or long-term incentives (LTI), which may be discretionary or non-discretionary based on the plan.