Applied Researcher II
Not Disclosed•Full-TimeOn-site
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, we have 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. We are committed to building world-class applied science and engineering teams to reimagine how we serve our customers and businesses.
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 collaborate closely with product, technology, and business leaders to apply the state of the art in AI to our business, ensuring that emerging capabilities transform customer experiences.
As an Applied Researcher II, you will partner with a cross-functional team of data scientists, software engineers, and product managers to deliver AI-powered products. You will leverage a broad stack of technologies to reveal insights hidden within huge volumes of numeric and textual data, building AI foundation models through all phases of development from design through implementation.
This role is designed for those who love the process of analyzing and creating, with a shared passion for doing the right thing for our customers. You will engage in high-impact applied research to push the latest AI developments into the next generation of customer experiences. Success in this role requires an innovative, creative, and technical mindset, where you challenge conventional thinking and translate the complexity of your work into tangible business goals.
Capital One is committed to a fair and inclusive hiring process. We consider qualified applicants regardless of background. For specific details on the interview stages and timeline, please refer to the application portal or contact our recruiting team.
Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. We promote a drug-free workplace and consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws.
If you require an accommodation during the application or interview process, please contact Capital One Recruiting at 1-800-304-9102 or via email at RecruitingAccommodation@capitalone.com. All information provided will be kept confidential.
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, optimization). Membership in a team that 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 focus on geometric deep learning (Graph Neural Networks, Sequential Models, Multivariate Time Series). Multiple papers on training models on graph and sequential data structures at KDD, ICML, NeurIPs, or ICLR. Experience 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 open-source frameworks (pytorch-geometric, DGL). Proposed new methods for inference or representation learning on graphs or sequences. Experience with datasets of 100m+ users. 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 transfer learning, model adaptation, and model guidance. Experience deploying a fine-tuned large language model. PhD focused on adversarial machine learning, red teaming, and model alignment. Deep expertise in limit seeking security research (e.g., prompt injection, model inversion, RAG poisoning). Proven track record of developing scalable evaluation suites and automated red teaming frameworks. Foundational research in high-stakes AI deployment bridging explainability, reliability, and fine-tuning. Active contributor to the AI Safety discourse.
AIS (Applied Information Sciences) • San Diego, California
University of Utah - Employment • Murray, Utah
Mission Technologies, a division of HII • Virginia Beach, Virginia
Skills: Machine Learning, Ai, Pytorch, Aws, Huggingface, Lightning, Vectordbs, 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; PhD in Computer Science, Machine Learning, Computer Engineering, Applied Mathematics, Electrical Engineering, or related fields preferred.