Applied Researcher 4

Capital One • San Jose, CA

4d • $218,700 - $272,300

About The Position

Capital One’s AI Foundations team develops trustworthy and reliable AI systems and works across the research lifecycle, from academic partnerships to production systems. The team collaborates with product, technology, and business leaders to apply state-of-the-art AI to the company’s business.

The Applied Researcher partners with data scientists, software engineers, machine learning engineers, and product managers to build AI-powered products. The role applies a broad technology stack—including PyTorch, AWS Ultraclusters, Hugging Face, and Lightning—to analyze large volumes of numeric and textual data. It develops AI foundation models from design through training, evaluation, validation, and implementation, and conducts applied research to bring emerging AI developments into customer experiences. The researcher also communicates technical work in terms of business goals and collaborates with senior researchers on internal technical reports or conference submissions.

Candidates are expected to bring strong AI foundations, experience building and deploying large-scale deep learning models, and the ability to independently pursue an impactful research agenda. The role is suited to someone who combines research creativity and technical depth with an engineering mindset and a focus on customer outcomes.

Requirements

• Currently have or be in the process of obtaining a PhD in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or a related field; the degree must be obtained on or before the scheduled start date; alternatively, have an M.S. in one of these fields or a related field and at least 2 years of experience in Applied Research.
• Deep understanding of the foundations of AI methodologies.
• Hands-on experience developing AI foundation models and solutions using open-source tools and cloud computing platforms.
• Experience building large deep learning models involving language, images, events, or graphs.
• Expertise in at least one of training optimization, self-supervised learning, robustness, explainability, or RLHF.
• Track record of delivering models at scale in terms of training data and inference volumes.
• Experience delivering libraries, platform-level code, or solution-level code to existing products.
• Track record of high-quality machine-learning ideas or improvements, demonstrated by accomplishments such as first-author publications or projects.
• Ability to select impactful research problems and autonomously carry out long-running projects.
• Ability to communicate technical complexity in terms of business goals and work collaboratively across disciplines.

Nice To Haves

• PhD in Computer Science, Machine Learning, Computer Engineering, Applied Mathematics, Electrical Engineering, or a related field.
• Behavioral models experience.
• PhD focus on geometric deep learning, including graph neural networks, sequential models, or multivariate time series.
• Multiple papers on training models for graph and sequential data structures at KDD, ICML, NeurIPS, or ICLR.
• Experience scaling graph models to more than 50 million nodes.
• Experience with large-scale deep-learning-based recommender systems.
• Experience in production real-time and streaming environments.
• Contributions to common open-source frameworks such as PyTorch Geometric or DGL.
• Proposed new methods for inference or representation learning on graphs or sequences.
• Experience working with datasets containing more than 100 million 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.

Responsibilities

• Partner with data scientists, software engineers, machine learning engineers, and product managers to deliver AI-powered customer products.
• Use technologies such as PyTorch, AWS Ultraclusters, Hugging Face, and Lightning to find insights in large volumes of numeric and textual data.
• Build AI foundation models through design, training, evaluation, validation, and implementation.
• Conduct applied research that translates emerging AI developments into new customer experiences.
• Translate complex technical work into tangible business goals.
• Collaborate with senior researchers on internal technical reports or conference submissions describing methods or findings.
• Research and evaluate emerging technologies and identify opportunities to apply state-of-the-art methods.
• Own and pursue a research agenda, including selecting impactful problems and carrying out long-running projects.

Benefits

• Comprehensive, competitive, and inclusive health, financial, and other benefits supporting total well-being.
• Eligible for performance-based incentive compensation, which may include cash bonuses and/or long-term incentives.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: Ph.D. or professional degree