Applied Researcher 4

Capital One • San Jose, CA

13h • $218,700 - $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 with product, technology, and business leaders. The team builds AI-powered products and scalable AI infrastructure to improve customer and business experiences.

The Applied Researcher partners with data scientists, software engineers, machine learning engineers, and product managers to develop AI foundation models and products. The role spans model design, training, evaluation, validation, and implementation, and applies emerging AI research to customer experiences. Researchers communicate technical work in terms of business goals and collaborate on internal technical reports and conference submissions.

The position requires a strong foundation in AI methodologies, hands-on experience developing foundation models and solutions with open-source tools and cloud platforms, and a record of delivering models at scale. Candidates should be able to identify impactful research problems and independently pursue long-running research projects. Capital One will consider sponsoring a qualified applicant for employment authorization. The role is eligible for performance-based incentive compensation, which may include cash bonuses and/or long-term incentives. Salary varies by work location: $218,700–$249,600 in Cambridge, MA and McLean, VA; and $238,600–$272,300 in New York, NY, San Francisco, CA, and San Jose, CA.

Requirements

• Currently have or be pursuing a PhD in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or a related field, with the required degree obtained by the scheduled start date; alternatively, a master's degree in one of these fields or a related field plus two 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 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.
• A 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.
• A record of high-quality machine learning ideas or improvements, demonstrated by accomplishments such as first-author publications or projects.
• Ability to choose impactful research problems and autonomously 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 five 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 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 algorithms and/or optimizer design.
• Experience with compiler design.
• PhD focused on guiding LLMs with further tasks, such as supervised finetuning, instruction tuning, dialogue finetuning, or parameter tuning.
• Demonstrated knowledge of transfer learning, model adaptation, and model guidance.
• Experience deploying a fine-tuned large language model.

Responsibilities

• Partner with data scientists, software engineers, machine learning engineers, and product managers to deliver AI-powered products.
• Use AI technologies and tools to identify insights in 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.
• Collaborate with senior researchers on internal technical reports and conference submissions.
• Develop and pursue an impactful research agenda, including independently carrying out long-running projects.
• Deliver models at scale and contribute libraries, platform-level code, or solution-level code to existing products.

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