Junior Applied Research Scientist
2 weeks ago
Position: Applied Researcher I
Overview:
At Capital One, we are pioneering trustworthy and dependable AI systems, transforming the banking landscape for the better. For years, Capital One has been at the forefront of utilizing machine learning to develop real-time, intelligent, automated customer interactions. From alerting customers about unusual transactions to providing instant responses to inquiries, our applications of AI and ML are enhancing the human experience in banking. We are dedicated to assembling top-tier applied science and engineering teams, continuously advancing our industry-leading capabilities with innovative product experiences and scalable, high-performance AI infrastructure. At Capital One, you will play a crucial role in harnessing the transformative potential of emerging AI technologies to redefine how we serve our customers and businesses who appreciate the products and services we create.
Team Overview:
The AI Foundations team is integral to realizing our vision for AI at Capital One. Our initiatives encompass every facet of the research lifecycle, from collaborating with academic institutions to developing production systems. We engage with product, technology, and business leaders to implement cutting-edge AI solutions in our operations.
Key Responsibilities:
- Collaborate with a diverse team of data scientists, software engineers, machine learning engineers, and product managers to deliver AI-driven products that revolutionize customer interactions with their finances.
- Utilize a comprehensive technology stack - including Pytorch, AWS Ultraclusters, Huggingface, Lightning, VectorDBs, and more - to uncover insights from vast amounts of numerical and textual data.
- Develop AI foundational models through all stages of the development 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 clear business objectives.
- Passionate about analysis and creation, with a commitment to making ethical decisions that benefit our customers.
- Innovative thinker who actively researches and assesses emerging technologies, staying updated on state-of-the-art methods and applications.
- Creative problem-solver who enjoys defining and tackling ambiguous challenges, eager to share new ideas.
- A leader who questions conventional wisdom and collaborates with stakeholders to enhance existing processes.
- Technically proficient with open-source languages and enthusiastic about further development, possessing hands-on experience in creating AI foundational models using open-source tools and cloud platforms.
- Deep understanding of AI methodologies and their foundational principles.
- Experience in building extensive deep learning models across various domains, with expertise in areas such as training optimization, self-supervised learning, robustness, and explainability.
- Engineering mindset demonstrated by a history of delivering scalable models in terms of training data and inference volumes.
- Proven track record of contributing to libraries, platform-level code, or solution-level code for existing products.
- A professional with a history of generating high-quality ideas or enhancing existing concepts in machine learning, evidenced by notable publications or projects.
- Ability to independently manage a research agenda, selecting impactful research questions and executing long-term projects autonomously.
- Currently pursuing or in the process of obtaining a PhD, with the expectation of completion by the scheduled start date, or an M.S. with a minimum of 2 years of experience in Applied Research.
- PhD in Computer Science, Machine Learning, Computer Engineering, Applied Mathematics, Electrical Engineering, or related fields.
- Experience with large language models and publications related to their pre-training and optimization.
- Background in geometric deep learning and contributions to significant open-source frameworks.
- Knowledge of optimization techniques for large deep learning models and experience with compiler design.
- Familiarity with data preparation methodologies and contributions to major open-source datasets.
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