
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 to change banking for good. For years, we have led the industry in using machine learning to create real-time, intelligent, automated customer experiences. From informing customers about unusual charges to answering their questions instantly, our applications of AI & ML bring 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 with product, technology, and business leaders to apply state-of-the-art AI to our business, delivering AI-powered products that transform how customers interact with their money.
As an Applied Researcher II, you will engage in high-impact applied research to take the latest AI developments and push them into the next generation of customer experiences. You will partner with a cross-functional team of data scientists, software engineers, and product managers to deliver solutions that reveal insights hidden within huge volumes of numeric and textual data.
In this role, you will build AI foundation models through all phases of development, from design through training, evaluation, validation, and implementation. You will flex your interpersonal skills to translate the complexity of your work into tangible business goals, ensuring that every decision made is the right one for our customers. You will own and pursue a research agenda, choosing impactful problems and autonomously carrying out long-running projects.
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. We promote a drug-free workplace and consider for employment qualified applicants with a criminal history in a manner consistent with applicable laws. We are committed to building a diverse and inclusive culture where talent development is a passion for our team and beyond.
Work model: On-site
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 4 years experience; PhD in Computer Science, Machine Learning, Computer Engineering, Applied Mathematics, Electrical Engineering, or related fields preferred.
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 containing 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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