Staff Applied Researcher (Remote Eligible)

Capital One • San Jose, CA

6d • $306,300 - $381,300

About The Position

Capital One is hiring a Staff Applied Researcher for its AI Foundations team. The team works across the AI research life cycle, from academic partnerships to production systems, applying state-of-the-art AI in collaboration with product, technology, and business leaders. This is an individual-contributor role that drives strategic direction through collaboration with Applied Science, Engineering, and Product leaders. The researcher will guide and mentor applied scientists and their managers without directly managing them, and represent Capital One in the research community, including collaboration with prominent faculty members.

The role partners with data scientists, software engineers, machine learning engineers, and product managers to build AI-powered customer products. It involves developing AI foundation models from design through training, evaluation, validation, and implementation; pursuing high-impact applied research; and translating technical work into business goals. The researcher will also define and steward research directions that extend the organization’s scientific agenda and turn foundational discoveries into deployable capabilities. Technologies mentioned include PyTorch, AWS Ultraclusters, Hugging Face, and Lightning.

The role is open to remote or hybrid work. For hybrid employees, Capital One’s hybrid policy applies; remote employees are expected to follow its Remote Worker Expectations. The position may be eligible for performance-based incentive compensation, including cash bonuses and/or long-term incentives. Capital One states that it may sponsor a qualified new applicant for employment authorization.

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 for language, images, events, or graphs, with expertise in at least one of training optimization, self-supervised learning, robustness, explainability, or 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.
• Record of high-quality machine-learning ideas or improvements, 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.
• Ability to collaborate across disciplines, communicate technical work in business terms, and guide and mentor others.

Nice To Haves

• PhD in Computer Science, Machine Learning, Computer Engineering, Applied Mathematics, Electrical Engineering, or a related field.
• For LLM research: PhD focus on NLP or a master’s degree with 10 years of industrial NLP research experience; core contribution to training an LLM from scratch with 10B+ parameters and 500B+ tokens; numerous relevant publications at ACL, NAACL, EMNLP, NeurIPS, ICML, or ICLR; work on an LLM currently available for use; technical leadership guiding a large-scale model training team; experience with 500+ node GPU clusters or models scaled to 70B parameters and 1T+ tokens; or experience with training optimization frameworks such as DeepSpeed or NeMo.
• For behavioral models: PhD focus in geometric deep learning, graph neural networks, sequential models, or multivariate time series; technical leadership in deployment of a very large user behavior model; relevant publications at KDD, ICML, NeurIPS, or ICLR; experience scaling graph models beyond 50 million nodes, building large-scale deep-learning recommender systems, working in production real-time and streaming environments, contributing to PyTorch Geometric or DGL, proposing graph or sequence inference or representation-learning methods, or working with datasets of 100 million or more users.
• For training and inference optimization: PhD focus on optimizing training of very large language models; 5+ years of experience and/or publications in model sparsification, quantization, training parallelism or partitioning design, gradient checkpointing, or model compression.
• For fine-tuning: PhD focus on guiding LLMs with additional tasks such as supervised fine-tuning, instruction tuning, dialogue fine-tuning, or parameter tuning; knowledge of transfer learning, model adaptation, and model guidance; or experience deploying a fine-tuned LLM.
• For data preparation: numerous publications on tokenization, data quality, dataset curation, or labeling; leading contributions to an open-source corpus of 1 trillion or more tokens; or core contributions to open-source libraries for data quality, curation, or labeling.
• History of initiating and leading multi-year 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.
• Use AI technologies and large volumes of numeric and textual data to develop insights and solutions.
• Build AI foundation models through design, training, evaluation, validation, and implementation.
• Conduct high-impact applied research and apply emerging AI developments to customer experiences.
• Translate complex technical work into tangible business goals.
• Define and steward research directions that advance the organization’s long-term scientific agenda and produce deployable capabilities.
• Guide and mentor applied scientists and their managers without serving as their direct people leader.
• Represent Capital One in the research community and collaborate with prominent faculty members.
• Choose impactful research problems and independently pursue long-running research projects.

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.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior