
location_onNYU Paulson Center, 181, Mercer Street, University Village, Manhattan, New York County, New York, 10012, United States
At Capital One, we are creating trustworthy and reliable AI systems, changing banking for good. For years, Capital One has been leading the industry in using machine learning to create real-time, intelligent, automated customer experiences. From informing customers about unusual charges to answering their questions in real time, our applications of AI & ML are bringing humanity and simplicity to banking.
The AI Foundations team sits at the center of bringing this vision to life. Our work touches every aspect of the research life cycle, from partnering with academia to building production systems. We collaborate with product, technology, and business leaders to apply the state of the art in AI to our business, helping to reimagine how we serve the customers and businesses who have come to love the products and services we build.
Work model: On-site
NYU Paulson Center, 181, Mercer Street, University Village, Manhattan, New York County, New York, 10012, United States
New York, New York
PhD in Computer Science, Machine Learning, Computer Engineering, Applied Mathematics, or Electrical Engineering. LLM focus on NLP or Masters with 5 years of industrial NLP research experience. Multiple publications on pre-training of large language models (e.g., technical reports, SSL techniques, model pre-training optimization). Membership in a team that has trained a large language model from scratch (10B+ parameters, 500B+ tokens). Publications in deep learning theory. Publications at ACL, NAACL, EMNLP, Neurips, ICML, or ICLR. PhD focused on optimizing training of very large deep learning models. Multiple years of experience and/or publications on Model Sparsification, Quantization, Training Parallelism/Partitioning Design, Gradient Checkpointing, or 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 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.
Skills: Machine Learning, Pytorch, Aws, Huggingface, Lightning, Vector DBS, Ai Foundation Models, Deep Learning, LLM, NLP.
Education: PhD in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields required; Master's in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields with 4 years experience.