Applied Researcher 4 (AI Safety & Multimodal)

Capital One • McLean, NY

20h

About The Position

Capital One’s AI Foundations team develops trustworthy and reliable AI systems and applies state-of-the-art AI research to customer and business products. The team works across the research life cycle, partnering with academia and building production systems in collaboration with product, technology, and business leaders.

The Applied Researcher will collaborate with data scientists, software engineers, machine learning engineers, and product managers to build AI-powered products. The role includes developing AI foundation models from design through training, evaluation, validation, and implementation; conducting applied research to bring emerging AI developments into customer experiences; and communicating research findings in terms of business goals. The researcher will also work with senior researchers on internal technical reports or conference submissions.

The position emphasizes AI safety and multimodal systems, including red teaming and safety guardrails for vision-language and multimodal language models and multimodal retrieval-augmented generation architectures. Researchers are expected to bring an independent research agenda, develop and deliver models and code for products at scale, and contribute high-quality ideas and research findings.

The role is offered at listed locations including Cambridge, Massachusetts; McLean, Virginia; New York, New York; and San Jose, California. The role is eligible for performance-based incentive compensation. Capital One states that it may sponsor a qualified applicant for employment authorization.

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; 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 Applied Research experience.
• Deep understanding 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.
• 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 terms of 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 by accomplishments such as first-author publications or projects.
• Ability to own and pursue a research agenda, choose impactful research problems, and autonomously carry out long-running projects.

Nice To Haves

• PhD in Computer Science, Machine Learning, Computer Engineering, Applied Mathematics, Electrical Engineering, or a related field.
• PhD focused 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 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 or at ACL, NAACL, EMNLP, NeurIPS, ICML, or ICLR.
• PhD focused on optimizing training of very large deep learning models.
• Multiple years of experience 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 or optimizer design.
• Experience with compiler design.
• PhD focused on guiding LLMs through 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.
• Ability to reproduce and extend peer-reviewed AI research using modern open-source frameworks.
• Experience in AI safety, LLM safety alignment, or guardrail development.
• Experience assessing the safety and vulnerabilities of multimodal applications using image or video alongside text.
• 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 AI and machine learning technologies to analyze large volumes of numeric and textual data.
• Build AI foundation models through design, training, evaluation, validation, and implementation.
• Conduct applied research and 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.
• Develop and pursue an independent research agenda, select impactful problems, and carry out long-running projects.
• Red-team multimodal systems and develop or deploy safety guardrails.
• Deliver models, libraries, platform-level code, or solution-level code for products at scale.

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