Applied Researcher II

Location: New York

* 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 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
* 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.
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