Senior Director, Applied Research

* 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
* At least 5 years of people leadership experience
* 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)
* 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
* 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
* PhD focused on topics related to guiding LLMs with further tasks (Supervised Fine tuning, Instruction-Tuning, Dialogue-Fine tuning, Parameter Tuning)
* Demonstrated knowledge of principles of transfer learning, model adaptation and model guidance
* Experience deploying a fine-tuned large language model

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 . Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level.
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