Applied Researcher 4 (AI Foundations, VLM Customization)

Capital One • San Jose, CA

4d

About The Position

Capital One’s AI Foundations team develops trustworthy and reliable AI systems, working across the research lifecycle from academic partnerships through production systems. The team collaborates with product, technology, and business leaders to apply state-of-the-art AI to customer and business needs.

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 applies emerging research to improve customer experiences. It also involves communicating research findings in business terms and collaborating with senior researchers on internal technical reports or conference submissions.

The position calls for a researcher with hands-on experience developing foundation models and AI solutions using open-source tools and cloud platforms, and a record of advancing machine-learning research and delivering models and code into products at scale. The role may focus on AI foundations and vision-language model customization.

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, with the degree obtained by the scheduled start date; or has an M.S. in one of these fields or a related field plus 2 years of Applied Research experience.
• 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 one or more of training optimization, self-supervised learning, robustness, explainability, or RLHF.
• Engineering track record delivering models at scale in training-data size and inference volume.
• 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 own a research agenda, choose impactful problems, and independently carry out long-running research projects.

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 on large language model pre-training, such as technical reports on pretrained LLMs, self-supervised learning techniques, or pre-training 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 fine-tuning topics related to 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.
• 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 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 and bring emerging AI developments into customer experiences.
• Translate technical work into tangible business goals.
• Collaborate with senior researchers to prepare internal technical reports or conference submissions summarizing novel methods or findings.
• Own and pursue a research agenda, including selecting impactful research problems and autonomously carrying out long-running projects.

Benefits

• Comprehensive health, financial, and other benefits supporting employee 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