Applied Researcher 4 (Red Teaming & Multimodal)

Capital One • San Jose, CA

1d • $218,700 - $272,300

About The Position

Capital One’s AI Foundations team develops trustworthy and reliable AI systems, working across the research life cycle from academic partnerships to production systems. The Applied Researcher 4 partners with data scientists, software and machine learning engineers, and product managers to build AI-powered products and improve how customers interact with their money.

The role applies current AI research to customer experiences and builds AI foundation models through design, training, evaluation, validation, and implementation. It includes research on red teaming and multimodal safety, scaling model training and inference, communicating findings to business stakeholders, and collaborating with senior researchers on internal technical reports or conference submissions.

Requirements

• Currently have or be in the process of obtaining a PhD in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or a related field; the required degree must be obtained on or before the scheduled start date. Alternatively, 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, with expertise in one or more of training optimization, self-supervised learning, robustness, explainability, or RLHF.
• Experience with red teaming and deploying safety guardrails for multimodal systems, including VLMs, MLLMs, and MM-RAG architectures.
• 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 generating high-quality machine learning ideas or improving existing ideas, demonstrated through accomplishments such as first-author publications or projects.
• Ability to identify impactful research problems and autonomously pursue long-running research projects.
• 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.

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 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.
• 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 and machine learning technologies to find insights in large volumes of numeric and textual data.
• Build AI foundation models through design, training, evaluation, validation, and implementation.
• Conduct applied research to apply emerging AI developments to 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 identifying impactful research problems and carrying out long-running projects.

Benefits

• Comprehensive health, financial, and other benefits supporting total well-being; eligibility varies by employment status and management level.
• 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