Staff Applied Researcher (Remote Eligible)

Capital One • McLean, VA

4d

About The Position

Capital One’s AI Foundations team advances the company’s AI vision across the research life cycle, from academic collaboration through production systems. This individual-contributor role helps set strategic direction in collaboration with Applied Science, Engineering, and Product leaders. The researcher guides and mentors applied scientists and their managers without serving as a direct people leader, and represents Capital One in the research community, including through collaboration with prominent faculty.

The role partners with data scientists, software engineers, machine learning engineers, and product managers to deliver AI-powered customer experiences. It applies tools including PyTorch, AWS Ultraclusters, Hugging Face, and Lightning to large numeric and textual datasets, and develops AI foundation models from design through training, evaluation, validation, and implementation. The researcher conducts high-impact applied research, translates technical work into business goals, and stewards research directions that connect foundational discoveries to deployable capabilities.

The position may be remote or hybrid. Capital One’s applicable remote-worker expectations or hybrid policy apply. Full-time annual salary ranges vary by location: Cambridge, MA and McLean, VA: $306,300–$349,500; New York, NY, San Francisco, CA, and San Jose, CA: $334,100–$381,300. The role is also eligible for performance-based incentive compensation, potentially including cash bonuses and/or long-term incentives. Capital One offers health, financial, and other benefits; eligibility varies by employment status and management level.

Requirements

• PhD in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or a related field, plus 4 years of experience in Applied Research; or an M.S. in one of these fields or a related field, plus 6 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, and 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 producing high-quality machine-learning ideas or improving existing ideas, demonstrated by accomplishments such as first-author publications or projects.
• Ability to own and pursue a research agenda, select impactful research problems, and autonomously carry out long-running projects.

Nice To Haves

• PhD in Computer Science, Machine Learning, Computer Engineering, Applied Mathematics, Electrical Engineering, or a related field.
• PhD focused on NLP, or a master’s degree plus 10 years of industrial NLP research experience.
• Core contributor to a team that trained a large language model from scratch with 10B+ parameters and 500B+ tokens.
• Numerous publications on large language model pre-training topics at ACL, NAACL, EMNLP, NeurIPS, ICML, or ICLR.
• Experience working on an open-source or commercial LLM currently available for use.
• Demonstrated ability to guide the technical direction of a large-scale model-training team.
• Experience working with GPU clusters of 500 or more nodes.
• Experience working on an LLM scaled to 70B parameters and 1T+ tokens.
• Experience with training optimization frameworks such as DeepSpeed or NeMo.
• PhD focused on geometric deep learning topics such as graph neural networks, sequential models, or multivariate time series.
• Technical leadership experience in deployment of a very large user behavior model.
• 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.
• PhD focused on optimizing training of very large language models.
• At least 5 years of experience and/or publications in model sparsification, quantization, training parallelism or partitioning design, gradient checkpointing, or model compression.
• PhD focused on guiding LLMs with further tasks, such as supervised fine-tuning, instruction tuning, dialogue fine-tuning, or parameter tuning.
• Knowledge of transfer learning, model adaptation, and model guidance.
• Experience deploying a fine-tuned large language model.
• Numerous publications studying tokenization, data quality, dataset curation, or labeling.
• Leading contributions to one or more large open-source corpora of 1 trillion or more tokens.
• Core contributor to open-source libraries for data quality, dataset curation, or labeling.
• History of initiating and leading multiyear research programs that shaped AI or ML subfields, such as representation learning, foundation models, or multimodal reasoning.

Responsibilities

• Partner with data scientists, software engineers, machine learning engineers, and product managers to deliver AI-powered products.
• Apply AI technologies to large volumes of numeric and textual data.
• Build AI foundation models through design, training, evaluation, validation, and implementation.
• Conduct applied research that brings emerging AI developments into customer experiences.
• Translate complex technical work into tangible business goals.
• Define and steward research directions that advance the organization’s scientific agenda and translate discoveries into deployable capabilities.
• Guide and mentor applied scientists and their managers without directly managing them.
• Represent Capital One in the research community and collaborate with prominent faculty members.
• Collaborate with Applied Science, Engineering, and Product leaders to drive strategic direction.

Benefits

• Comprehensive health, financial, and other benefits supporting total well-being; eligibility varies by employment status and management level.
• 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