Applied Researcher 4

About the Role

At Capital One, we are creating trustworthy and reliable AI systems. 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. You will help bring the transformative power of emerging AI capabilities to reimagine how we serve our customers and businesses.

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.

Key Responsibilities

• Partner with a cross-functional team of data scientists, software engineers, machine learning engineers, and product managers to deliver AI-powered products.
• Leverage a broad stack of technologies — Pytorch, AWS Ultraclusters, Huggingface, Lightning, and more — to reveal 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.
• Collaborate with senior researchers to prepare internal tech reports or conference submissions summarizing novel methods or findings.

Requirements & Tech Stack

• Pytorch
• AWS Ultraclusters
• Huggingface
• Lightning
• Open-source languages and tools

Qualifications

• Currently has, or is in the process of obtaining, a PhD in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields, with an exception that the required degree will be obtained on or before the scheduled start date, or an M.S. in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields plus 2 years of experience in Applied Research.
• Innovative: Continually research and evaluate emerging technologies, stay current on published state-of-the-art methods, technologies, and applications, and seek out opportunities to apply them.
• Creative: Thrive on bringing definition to big, undefined problems and asking questions to find answers.
• A leader: Challenge conventional thinking and work with stakeholders to identify and improve the status quo.
• Technical: Comfortable with open-source languages and passionate about developing further.
• 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.
• 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 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.
• Ability to own and pursue a research agenda, including choosing impactful research problems and autonomously carrying out long-running projects.

Preferred Qualifications:

• PhD in Computer Science, Machine Learning, Computer Engineering, Applied Mathematics, Electrical Engineering, or related fields.
• Behavioral Models.
• PhD focus on topics in geometric deep learning (Graph Neural Networks, Sequential Models, Multivariate Time Series).
• Multiple papers on topics relevant to training models on graph and sequential data structures at KDD, ICML, NeurIPs, ICLR.
• Worked on scaling graph models to greater than 50m nodes.
• Experience with large-scale deep learning-based recommender systems.
• Experience with production real-time and streaming environments.
• Contributions to common open-source frameworks (pytorch-geometric, DGL).
• Proposed new methods for inference or representation learning on graphs or sequences.
• Worked with datasets with 100m+ users.
• Demonstrated ability to reproduce and extend peer-reviewed AI research using modern open-source frameworks.
• Experience designing controlled experiments and documenting reproducibility results for internal or public research.