Applied Researcher II

Overview

The AI Foundations team is at the center of bringing our vision for AI at Capital One to life. Our work touches every aspect of the research life cycle, from partnering with academia to building production systems. We work with product, technology, and business leaders to apply the state of the art in AI to our business.

Responsibilities

• Partner with a cross-functional team of data scientists, software engineers, machine learning engineers, and product managers to deliver AI-powered products that change how customers interact with their money.
• Leverage a broad stack of technologies—PyTorch, AWS UltraClusters, Huggingface, Lightning, VectorDBs, and more—to reveal insights hidden within huge volumes of numeric and textual data.
• Build AI foundation models through all phases of development, from design through training, evaluation, validation, and implementation.
• Engage in high-impact applied research to take the latest AI developments and push them into the next generation of customer experiences.
• Flex your interpersonal skills to translate the complexity of your work into tangible business goals.

Qualifications

• You love the process of analyzing and creating, but also share our passion to do the right thing. You know at the end of the day it’s about making the right decision for our customers.
• Innovative. You continually research and evaluate emerging technologies. You stay current on published state-of-the-art methods, technologies, and applications and seek out opportunities to apply them.
• Creative. You thrive on bringing definition to big, undefined problems. You love asking questions and pushing hard to find answers. You’re not afraid to share a new idea.
• A leader. You challenge conventional thinking and work with stakeholders to identify and improve the status quo. You’re passionate about talent development for your own team and beyond.
• Technical. You’re comfortable with open-source languages and are passionate about developing further. You have hands-on experience developing AI foundation models and solutions using open-source tools and cloud computing platforms. You have a deep understanding of the foundations of AI methodologies.
• Experience building large deep learning models, whether on language, images, events, or graphs, as well as expertise in one or more of the following: training optimization, self-supervised learning, robustness, explainability, RLHF.
• An engineering mindset as shown by a track record of delivering models at scale both in terms of training data and inference volumes. Experience in delivering libraries, platform-level code, or solution-level code to existing products.
• A professional with a track record of coming up with new ideas or improving upon existing ideas in machine learning, demonstrated by accomplishments such as first-author publications or projects.
• Possess the ability to own and pursue a research agenda, including choosing impactful research problems and autonomously carrying out long-running projects.

Preferred Qualifications

• PhD in Computer Science, Machine Learning, Computer Engineering, Applied Mathematics, Electrical Engineering, or related fields.
• LLM - PhD focus on NLP or Masters with 5 years of industrial NLP research experience.
• Multiple publications on topics related to the pre-training of large language models (e.g., technical reports of pre-trained LLMs, SSL techniques, model pre-training optimization).
• Member of team that has trained a large language model from scratch (10B + parameters, 500B+ tokens).
• Publications in deep learning theory.
• Publishations at ACL, NAACL, EMNLP, NeurIPS, ICML, or ICLR.
• Optimization (Training & Inference)—PhD focused on topics related to optimizing training of very large deep learning models.
• Multiple years of experience and/or publications on one of the following topics: model sparsification, quantization, training parallelism/partitioning design, gradient checkpointing, model compression.
• Experience optimizing training for a 10B+ model.
• Deep knowledge of deep learning algorithmic and/or optimizer design.
• Experience with compiler design.
• Finetuning—PhD focused on topics related to guiding LLMs with further tasks (supervised finetuning, instruction-tuning, dialogue-finetuning, parameter tuning).
• Demonstrated knowledge of principles of transfer learning, model adaptation, and model guidance.
• Experience deploying a fine-tuned large language model.