Description
At Capital One, we are creating trustworthy and reliable AI systems, changing banking for good.
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.
As a Staff Applied Researcher, you will drive strategic direction through collaboration with Applied Science, Engineering, and Product leaders across Capital One. 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, Huggingface, Lightning, 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.
- Define and steward novel research directions that expand the organization's long-term scientific agenda, ensuring foundational discoveries translate into deployable capabilities.
Requirements:
- 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.
- 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.
- An engineering mindset as shown by a track record of delivering models at scale both in terms of training data and inference volumes.
- Experience in delivering libraries, platform-level code, or solution-level code to existing products.
- A professional with a track record of coming up with high-quality ideas or improving upon existing ideas in machine learning, demonstrated by accomplishments such as first-author publications or projects.
- Possess the ability to own and pursue a research agenda, including choosing impactful research problems and autonomously carrying out long-running projects.
Benefits:
- Comprehensive, competitive, and inclusive set of health, financial, and other benefits that support your total well-being.
Salary:
- Cambridge, MA: $306,300 - $349,500 per year
- McLean, VA: $306,300 - $349,500 per year
- New York, NY: $334,100 - $381,300 per year
- San Francisco, CA: $334,100 - $381,300 per year
- San Jose, CA: $334,100 - $381,300 per year