
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 bring the transformative power of emerging AI capabilities 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 our research translates into tangible business goals and next-generation customer experiences.
As an Applied Researcher I, you will partner with a cross-functional team of data scientists, software engineers, machine learning 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. Your work will involve building AI foundation models through all phases of development, from design through training, evaluation, validation, and implementation. You will engage in high-impact applied research to take the latest AI developments and push them into the next generation of customer experiences, flexing your interpersonal skills to translate complex technical work into clear value.
We are looking for individuals who love the process of analyzing and creating, but also share our passion to do the right thing. We value those who are innovative, continually researching and evaluating emerging technologies to stay current on state-of-the-art methods. We seek creative thinkers who thrive on bringing definition to big, undefined problems and are not afraid to share new ideas. As leaders, we expect you to challenge conventional thinking, work with stakeholders to improve the status quo, and be passionate about talent development for your own team and beyond.
Candidates hired to work in other locations will be subject to the pay range associated with that location. This role is expected to accept applications for a minimum of 5 business days. No agencies please.
Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace and will 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 recruiting process, please contact Capital One Recruiting.
Skills: Machine Learning, Ai, Pytorch, Aws, Huggingface, Lightning, Vector DBS, 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 2 years of experience.
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. Publications studying tokenization, data quality, dataset curation, or labeling. Contribution to a major open source corpus. Contribution to open source libraries for data quality, dataset curation, or labeling.
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