Applied Researcher 5
About The Position
Capital One’s AI Foundations team develops trustworthy and reliable AI systems and works across the research life cycle, from academic partnerships to production systems. The team partners with product, technology, and business leaders to apply state-of-the-art AI to the business. The Applied Researcher partners with data scientists, software engineers, machine learning engineers, and product managers to deliver AI-powered products. The role uses technologies such as PyTorch, AWS Ultraclusters, Hugging Face, and Lightning to analyze large volumes of numeric and textual data. It builds AI foundation models from design through training, evaluation, validation, and implementation, and applies emerging AI research to customer experiences. The researcher translates technical work into business goals and leads cross-functional research efforts that connect prototype development and deployment with ML engineering partners. The ideal candidate is innovative, creative, technically strong, and committed to making sound decisions for customers. They have a deep understanding of AI methodologies, hands-on experience with foundation models and open-source tools and cloud platforms, and an engineering mindset for delivering models at scale. They can own and pursue a research agenda, choose impactful problems, and carry out long-running projects. The role requires a qualifying PhD with two years of applied research experience, or a relevant master’s degree with four years of applied research experience, subject to the stated degree-completion exception.
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, with the stated exception that the required degree may be obtained on or before the scheduled start date plus two years of Applied Research experience; alternatively, a relevant master's degree plus four years of Applied Research experience.
- Deep understanding of the foundations of AI methodologies.
- Experience building large deep learning models involving language, images, events, or graphs, and expertise in one or more of training optimization, self-supervised learning, robustness, explainability, or RLHF.
- 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 own and pursue a research agenda, select impactful research problems, and autonomously carry out long-running projects.
- Hands-on experience developing AI foundation models and solutions using open-source tools and cloud computing platforms.
Nice To Haves
- PhD in Computer Science, Machine Learning, Computer Engineering, Applied Mathematics, Electrical Engineering, or a related field.
- PhD focus in NLP, or a master's degree with five years of industrial NLP research experience.
- Multiple publications related to pre-training large language models, such as technical reports on pre-trained LLMs, self-supervised learning techniques, or model pre-training optimization.
- 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 customer products.
- Use technologies including PyTorch, AWS Ultraclusters, Hugging Face, and Lightning to uncover insights in large volumes of numeric and textual data.
- Build AI foundation models through design, training, evaluation, validation, and implementation.
- Apply emerging AI developments through high-impact applied research to create new customer experiences.
- Translate technical work into tangible business goals.
- Lead cross-functional research efforts that bridge prototype model development and deployment with ML engineering partners, translating scientific insights into production impact.
Benefits
- Comprehensive health, financial, and other benefits supporting total well-being.
- Eligibility for performance-based incentive compensation, which may include cash bonuses and/or long-term incentives.
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What This Job Offers
Job Type
Full-time
Career Level
Senior
Education Level
Ph.D. or professional degree
