Applied Researcher 4 (AI Foundations: Recommendation Systems)

Capital One • San Jose, CA

1d • $218,700 - $249,600

About The Position

Capital One’s AI Foundations team develops trustworthy and reliable AI systems, applying state-of-the-art research to customer and business needs. The team’s work spans the research life cycle, from academic partnerships to production systems, in collaboration with product, technology, and business leaders.

The Applied Researcher will partner with data scientists, software engineers, machine learning engineers, and product managers to develop AI-powered products. The role involves using technologies such as PyTorch, AWS Ultraclusters, Hugging Face, and Lightning to analyze numeric and textual data; developing AI foundation models from design through training, evaluation, validation, and implementation; and conducting applied research that advances customer experiences. The researcher will communicate technical work in terms of business goals and collaborate with senior researchers on internal technical reports or conference submissions.

The ideal candidate combines deep AI expertise and an engineering mindset with creativity, leadership, and a commitment to customer-focused decisions. They have experience building large deep-learning models and delivering models at scale, along with a record of meaningful research contributions. They can independently choose impactful research problems and pursue long-running research projects.

Requirements

• Currently has or is 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. Alternatively, an M.S. in one of these fields or a related field plus 2 years of experience in applied research is required.
• 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 in areas such as 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 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, including choosing impactful problems and autonomously carrying out long-running projects.

Nice To Haves

• PhD in Computer Science, Machine Learning, Computer Engineering, Applied Mathematics, Electrical Engineering, or a related field.
• PhD focus in NLP, or a master’s degree with 5 years of industrial NLP research experience.
• Multiple publications related to large language model pre-training, such as technical reports on pretrained LLMs, self-supervised learning techniques, or pre-training optimization.
• Experience as a member of 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 focused on optimizing training of very large deep-learning models.
• Multiple years of experience and/or publications in model sparsification, quantization, training parallelism or partitioning design, gradient checkpointing, or model compression.
• Experience optimizing training for a 10B+ model.
• Deep knowledge of deep-learning algorithmic and/or optimizer design.
• Experience with compiler design.
• 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 principles.
• Experience deploying a fine-tuned large language model.
• 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 uncover insights in large volumes of numeric and textual data.
• Build AI foundation models through design, training, evaluation, validation, and implementation.
• Conduct applied research to translate 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 summarizing novel methods or findings.
• Choose impactful research problems and autonomously pursue long-running research projects.

Benefits

• Comprehensive, competitive, and inclusive 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