Senior Director, Applied Research

About the role

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 platforms and solutions that change how customers interact with their money.
• 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.
• Leverage a broad stack of technologies — Pytorch, AWS Ultraclusters, Huggingface, Lightning, VectorDBs, and more — to reveal the insights hidden within huge volumes of numeric and textual data.
• Flex your interpersonal skills to translate the complexity of your work into tangible business goals.

Requirements

• PhD in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields plus 6 years of experience in Applied Research
• M.S. in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields plus 8 years of experience in Applied Research

Qualifications

• Basic Qualifications: PhD in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields plus 6 years of experience in Applied Research or M.S. in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields plus 8 years of experience in Applied Research
• Preferred Qualifications: LLM, PhD focus on NLP or Masters with 10 years of industrial NLP research experience, Core contributor to team that has trained a large language model from scratch (10B + parameters, 500B+ tokens), Numerous publications at ACL, NAACL and EMNLP, Neurips, ICML or ICLR 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), Has worked on an LLM (open source or commercial) that is currently available for use, Demonstrated ability to guide the technical direction of a large-scale model training team, Experience working with 500+ node clusters of GPUs, Has worked on LLM scaled to 70B parameters and 1T+ tokens, Experience with common training optimization frameworks (deep speed, nemo), Behavioral Models, PhD focus on topics in geometric deep learning (Graph Neural Networks, Sequential Models, Multivariate Time Series), Member of technical leadership for model deployment for a very large user behavior model, Multiple papers on topics relevant to training models on graph and sequential data structures at KDD, ICML, NeurIPs, ICLR, Worked on scaling graph models to greater than 50m nodes, Experience with large scale deep learning based recommender systems, Experience with production real-time and streaming environments, Contributions to common open source frameworks (pytorch-geometric, DGL), Proposed new methods for inference or representation learning on graphs or sequences, Worked datasets with 100m+ users, Optimization (Training & Inference), PhD focused on topics related to optimizing training of very large language models, 5+ years of experience and/or publications on one of the following topics: Model Sparsification, Quantization, Training Parallelism/Partitioning Design, Gradient Checkpointing, Model Compression, 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, Data Preparation, Numerous Publications studying tokenization, data quality, dataset curation, or labeling, Leading contributions to one or more large open source corpus (1 Trillion + tokens), Core contributor to open source libraries for data quality, dataset curation, or labeling

Benefits

Capital One offers a comprehensive, competitive, and inclusive set of health, financial, and other benefits that support your total well-being. Learn more at the Capital One Careers website.

Pay

Salaries for this role are listed below, by location:

• Sales Territory: $318,100 - $363,100
• Cambridge, MA: $350,000 - $399,500
• McLean, VA: $350,000 - $399,500
• New York, NY: $381,800 - $435,700
• Richmond, VA: $318,100 - $363,100
• San Francisco, CA: $381,800 - $435,700

Schedule

This role is full-time.