Applied Researcher 4 (AI Foundations, VLM Customization)

Capital One • San Jose, CA

6d • $238,600 - $272,300

About The Position

Capital One’s AI Foundations team works across the AI research life cycle, from academic partnerships to production systems, applying state-of-the-art AI to the business. The Applied Researcher 4 partners with data scientists, software and machine learning engineers, and product managers to develop AI-powered products that improve how customers interact with their finances.

The role involves developing AI foundation models through design, training, evaluation, validation, and implementation, and conducting applied research to bring emerging AI advances into customer experiences. The researcher uses technologies including PyTorch, AWS Ultraclusters, Hugging Face, and Lightning to analyze large volumes of numeric and textual data. The role also includes communicating research in terms of business goals and collaborating with senior researchers on internal technical reports or conference submissions.

The ideal candidate combines a strong foundation in AI methodologies with experience developing large deep learning models and delivering them at scale. They can own a research agenda, pursue long-running projects, and produce high-quality research ideas or improvements, while contributing libraries, platform code, or solutions to existing products.

Requirements

• Currently hold or be pursuing a PhD in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or a related field; the degree must be obtained by the scheduled start date, or hold an M.S. in one of these fields or a related field plus 2 years of Applied Research experience.
• Deep understanding of AI methodologies.
• Experience building large deep learning models involving language, images, events, or graphs.
• Experience in at least one of training optimization, self-supervised learning, robustness, explainability, or RLHF.
• Track record of delivering models at scale in terms of both 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 independently pursue long-running research projects.
• Hands-on experience developing AI foundation models and solutions using open-source tools and cloud computing platforms.

Nice To Haves

• PhD in Computer Science, Machine Learning, Computer Engineering, Applied Mathematics, Electrical Engineering, or a related field.
• PhD focus on NLP, or a master’s degree plus 5 years of industrial NLP research experience.
• Multiple publications related to pre-training large language models, such as technical reports on pretrained LLMs, self-supervised learning techniques, or model pretraining optimization.
• Membership on a team that trained a large language model from scratch with 10B+ parameters and 500B+ tokens.
• Publications in deep learning theory.
• Publications at ACL, NAACL, EMNLP, NeurIPS, ICML, or ICLR.
• PhD focus on guiding LLMs with further tasks, such as supervised fine-tuning, instruction tuning, dialogue fine-tuning, or parameter tuning.
• Demonstrated knowledge of transfer learning, model adaptation, and model guidance.
• Experience deploying a fine-tuned large language model.
• Publications studying tokenization, data quality, dataset curation, or labeling.
• Contribution to a major open-source corpus.
• Contribution to open-source libraries for data quality, dataset curation, or labeling.
• 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 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 across design, training, evaluation, validation, and implementation.
• Conduct applied research to bring emerging AI developments into customer experiences.
• Translate complex technical work into tangible business goals.
• Collaborate with senior researchers to prepare internal technical reports or conference submissions describing novel methods or findings.
• Own and pursue a research agenda, including selecting impactful research problems and independently carrying out long-running projects.

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