Applied Researcher II

* 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* Publications at ACL, NAACL and EMNLP, Neurips, ICML or ICLR* PhD focus on topics in geometric deep learning (Graph Neural Networks, Sequential Models, Multivariate Time Series)* 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 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* 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* PhD focused on topics related to adversarial machine learning, red teaming and model alignment.* Deep expertise in limit seeking security research, including deconstructing LLM architectures to identify novel attack surfaces like prompt injection, model inversion, and RAG poisoning.* Proven track record of developing scalable evaluation suites and automated red teaming frameworks to move emerging academic threats into practical, real world defensive applications.* Foundational research in high-stakes AI deployment, bridging the gap between AI Explainability, reliability, and the rigorous fine tuning required for real world use cases.* Active contributor to the AI Safety discourse, with the ability to document technical vulnerabilities and their direct impact on model privacy, alignment, and organizational risk.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. #J-18808-Ljbffr