Applied Researcher 5 (AI Foundations: Evaluation and Agentic Workflows)
About The Position
The Applied Researcher 5 will work on Capital One’s AI Foundations team to develop trustworthy, reliable AI systems and bring emerging AI capabilities into customer and business experiences. The team spans the research life cycle, from academic partnerships to production systems, and collaborates with product, technology, and business leaders to apply state-of-the-art AI. The role partners with data scientists, software engineers, machine learning engineers, and product managers to deliver AI-powered products. It involves building AI foundation models from design through training, evaluation, validation, and implementation; conducting applied research; and leading research threads that connect model prototypes with production deployment. The researcher will use technologies including PyTorch, AWS Ultraclusters, Hugging Face, and Lightning, and communicate research findings in terms of business goals and production impact. This is a hybrid position, subject to Capital One’s hybrid policy for required on-site presence at the designated office. The role may be based in Cambridge, McLean, New York, San Jose, or another location with a location-specific salary range. It is eligible for performance-based incentive compensation.
Requirements
- Currently has, or is in the process of obtaining, a PhD in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or a related field, plus 2 years of experience in Applied Research; alternatively, an M.S. in one of those fields or a related field, plus 4 years of experience in Applied Research. The required degree must be obtained on or before the scheduled start date.
- Deep understanding of the foundations of AI methodologies.
- Experience building large deep learning models involving language, images, events, or graphs.
- Expertise in one or more 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 generating high-quality machine learning ideas or improving existing ideas, demonstrated through accomplishments such as first-author publications or projects.
- Ability to own and pursue a research agenda, select impactful research problems, and independently carry out long-running projects.
- Hands-on experience developing AI foundation models and solutions using open-source tools and cloud computing platforms.
- Ability to collaborate with stakeholders, communicate research complexity in business terms, and lead cross-functional work.
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 on 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 with production real-time and streaming environments.
- Contributions to 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.
- Ability to design and lead small-scale research initiatives and guide junior researchers or engineers through experimental design and evaluation.
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 analyze large volumes of numeric and textual data.
- Build AI foundation models throughout their development lifecycle, including design, training, evaluation, validation, and implementation.
- Conduct applied research that advances AI capabilities for customer experiences.
- Translate complex research into tangible business goals.
- Lead cross-functional research threads connecting prototype model development and deployment with machine learning engineering partners.
- Choose impactful research problems and independently pursue a research agenda, including long-running projects.
- Guide junior researchers or engineers through research design and evaluation, where applicable.
Benefits
- Comprehensive health, financial, and other benefits supporting total well-being.
- Eligibility for performance-based incentive compensation, which may include cash bonuses and/or long-term incentives.
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What This Job Offers
Job Type
Full-time
Career Level
Senior
Education Level
Ph.D. or professional degree
