Applied Researcher

Description:

Capital One is seeking an Applied Researcher I to join its AI Foundations team in San Francisco, CA. This role is an exciting opportunity for researchers passionate about advancing artificial intelligence and machine learning in the financial technology space. The position focuses on building trustworthy, scalable, and innovative AI systems that transform the customer experience and redefine the future of banking.

Capital One has long been an industry leader in applying AI/ML to real-time, intelligent customer interactions such as fraud detection, personalized recommendations, and conversational AI. The AI Foundations team collaborates across product, engineering, research, and business functions to build state-of-the-art AI models, infrastructure, and solutions.

Key Responsibilities:

• Partner with cross-functional teams of researchers, engineers, and product managers to deliver cutting-edge AI-powered financial products.

• Build foundation AI models, from design through training, evaluation, validation, and large-scale deployment.

• Work on large-scale data, leveraging technologies such as PyTorch, AWS Ultraclusters, Hugging Face, Lightning, and VectorDBs.

• Conduct high-impact applied research to push the boundaries of AI and integrate emerging advancements into next-generation banking experiences.

• Translate complex research into practical solutions and business outcomes.

• Deliver research outcomes at scale, including libraries, platform-level code, and production-ready models.

• Publish and present research at leading AI/ML conferences and journals.

Ideal Candidate Profile:

• Strong foundation in AI and machine learning with a research-driven mindset.

• Innovative thinker, creative problem solver, and collaborative leader.

• Hands-on experience with deep learning model development at scale, including LLMs and foundation models.

• Knowledge of training optimization, self-supervised learning, explainability, robustness, and reinforcement learning from human feedback (RLHF).

• Proven record of publications or projects in applied ML/AI research.

• Ability to independently lead and pursue impactful research agendas.

Basic Qualifications:

• PhD (or in process of completion) in Computer Science, Electrical/Computer Engineering, Mathematics, AI, or related field; OR Master’s degree with 2+ years of applied research experience.

Preferred Qualifications:

• PhD with focus on NLP, large language models, optimization, or deep learning theory.

• Track record of training or optimizing large-scale LLMs (10B+ parameters, 500B+ tokens).

• Publications in top-tier conferences (NeurIPS, ICML, ICLR, ACL, EMNLP, NAACL).

• Expertise in optimization techniques (model sparsification, quantization, gradient checkpointing, model compression).

• Experience with supervised fine-tuning, instruction tuning, or transfer learning for LLMs.

• Industrial or academic contributions to compiler design, training efficiency, or large-scale model deployment.

Compensation & Benefits:

• Competitive salary $234000 – $267000 annually ($19500 – $22250 monthly).

• Performance-based incentives including bonuses and long-term equity.

• Comprehensive health, financial, and well-being benefits package.

• Opportunities for career growth, cutting-edge research, and professional development.