Applied Researcher 5

Capital One • McLean, VA

2d

About The Position

Capital One’s AI Foundations team develops trustworthy and reliable AI systems, spanning the research life cycle from academic partnerships to production systems. The team works with product, technology, and business leaders to apply state-of-the-art AI to banking and build scalable, high-performance AI infrastructure and customer experiences.

The Applied Researcher partners with data scientists, software engineers, machine learning engineers, and product managers to build AI-powered products. The role develops AI foundation models through design, training, evaluation, validation, and implementation, and conducts applied research to bring new AI developments into customer experiences. It also leads research threads that connect prototype model development with deployment, translating scientific insights into production impact and business goals.

The role calls for a technically strong, innovative researcher with hands-on experience building AI foundation models using open-source tools and cloud platforms. The researcher is expected to own and pursue a research agenda, identify impactful problems, carry out long-running projects, and help develop talent. Work may involve large deep-learning models and areas such as training optimization, self-supervised learning, robustness, explainability, and RLHF. The position is eligible for performance-based incentive compensation. Capital One will consider sponsoring a new qualified applicant for employment authorization.

Requirements

• Currently hold or be pursuing a Ph.D. in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or a related field, with the required degree obtained by the scheduled start date, plus 2 years of experience in Applied Research; or hold an M.S. in one of these fields or a related field, plus 4 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 one or more of training optimization, self-supervised learning, robustness, explainability, and RLHF.
• Engineering mindset and a track record of delivering models at scale in 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 through accomplishments such as first-author publications or projects.
• Ability to select impactful research problems and independently carry out long-running research projects.

Nice To Haves

• Ph.D. in Computer Science, Machine Learning, Computer Engineering, Applied Mathematics, Electrical Engineering, or a related field.
• Behavioral models: Ph.D. focus on geometric deep learning, including graph neural networks, sequential models, or multivariate time series.
• Behavioral models: Multiple papers on training models on graph and sequential data structures at KDD, ICML, NeurIPS, or ICLR.
• Behavioral models: Experience scaling graph models to more than 50 million nodes.
• Behavioral models: Experience with large-scale deep-learning-based recommender systems.
• Behavioral models: Experience with production real-time and streaming environments.
• Behavioral models: Contributions to open-source frameworks such as PyTorch Geometric or DGL.
• Behavioral models: Proposed new methods for inference or representation learning on graphs or sequences.
• Behavioral models: Experience working with datasets containing more than 100 million users.
• Optimization: Ph.D. focus on optimizing training of very large language models.
• Optimization: 5 or more years of experience and/or publications in model sparsification, quantization, training parallelism/partitioning design, gradient checkpointing, or model compression.
• Finetuning: Ph.D. focus on guiding LLMs with further tasks, such as supervised finetuning, instruction tuning, dialogue finetuning, or parameter tuning.
• Finetuning: Demonstrated knowledge of transfer learning, model adaptation, and model guidance.
• Finetuning: Experience deploying a fine-tuned large language model.
• Data preparation: Publications studying tokenization, data quality, dataset curation, or labeling.
• Data preparation: Contributions to a major open-source corpus.
• Data preparation: Contributions to open-source libraries for data quality, dataset curation, or labeling.
• Proven ability to design and lead small-scale research initiatives, guiding 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 products.
• Use technologies including PyTorch, AWS Ultraclusters, Hugging Face, and Lightning to analyze large volumes of numeric and textual data.
• Build AI foundation models through design, training, evaluation, validation, and implementation.
• Conduct applied research to translate recent AI developments into new customer experiences.
• Explain complex technical work in terms of tangible business goals.
• Lead cross-functional research threads connecting prototype model development and deployment with ML engineering partners.
• Own and pursue a research agenda, including selecting impactful research problems and carrying out long-running projects.
• Develop talent within and beyond the team.

Benefits

• Comprehensive health, financial, and other benefits supporting total well-being.
• 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