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Research Scientist I in Applied AI

2 months ago

Plano, Texas, United States Capital One Financial Corp Full time
Location: Center, United States of America

Position: Applied Researcher I

Overview:
At Capital One, we are dedicated to developing trustworthy and efficient AI systems that transform the banking experience. Our leadership in the industry harnesses machine learning to create intelligent, automated customer interactions. From alerting customers about unusual transactions to providing real-time assistance, our AI and ML applications enhance the simplicity and humanity of banking. We are committed to assembling top-tier applied science and engineering teams to advance our capabilities with innovative product experiences and robust AI infrastructure. At Capital One, you will play a pivotal role in leveraging cutting-edge AI technologies to redefine how we serve our customers and businesses who appreciate our offerings.

Team Overview:
The AI Foundations team is integral to realizing our AI vision at Capital One. Our efforts encompass every phase of the research lifecycle, from collaborating with academic institutions to constructing production systems. We partner with product, technology, and business leaders to implement state-of-the-art AI solutions in our operations.

Key Responsibilities:
  • Collaborate with a diverse team of data scientists, software engineers, machine learning engineers, and product managers to create AI-driven products that enhance customer engagement with their finances.
  • Utilize a comprehensive array of technologies, including Pytorch, AWS Ultraclusters, Huggingface, Lightning, VectorDBs, and others, to extract insights from extensive datasets.
  • Develop AI foundational models through all stages of the process, from design to training, evaluation, validation, and deployment.
  • Conduct impactful applied research to integrate the latest AI advancements into next-generation customer experiences.
  • Employ your interpersonal skills to translate complex concepts into actionable business objectives.
Ideal Candidate Profile:
  • A passion for analysis and creativity, with a commitment to making decisions that prioritize customer welfare.
  • Innovative mindset, continuously exploring and assessing emerging technologies, and seeking opportunities for application.
  • Creative problem-solver, adept at defining and addressing significant, ambiguous challenges.
  • Leadership qualities, willing to challenge conventional wisdom and collaborate with stakeholders to enhance existing processes.
  • Technical proficiency with open-source programming languages and a strong desire for continuous development.
  • In-depth understanding of AI methodologies and principles.
  • Experience in constructing large deep learning models across various domains, with expertise in training optimization, self-supervised learning, robustness, explainability, and reinforcement learning from human feedback.
  • Engineering-oriented, demonstrated by a history of delivering scalable models in terms of both training data and inference loads.
  • Proven track record of contributing to libraries, platform-level code, or solution-level code for existing products.
  • A professional history of generating high-quality ideas or enhancing existing concepts in machine learning, evidenced by significant publications or projects.
  • Ability to independently pursue a research agenda, identifying impactful research questions and managing long-term projects.
Basic Qualifications:
  • Currently pursuing or in the process of obtaining a PhD, or holding an M.S. with a minimum of 2 years of experience in Applied Research.
Preferred Qualifications:
  • PhD in Computer Science, Machine Learning, Computer Engineering, Applied Mathematics, Electrical Engineering, or related fields.
  • Experience in NLP or large language models, with multiple publications related to pre-training techniques and model optimization.
  • Contributions to significant projects involving large language models and publications in reputable conferences.
  • Background in geometric deep learning or experience with large-scale recommender systems.
  • Expertise in optimizing training processes for extensive deep learning models.
  • Knowledge of principles related to model adaptation and deployment of fine-tuned models.
  • Contributions to open-source projects related to data quality and dataset curation.
Capital One is an equal opportunity employer committed to diversity and inclusion in the workplace.