Senior Director, Applied Research
Not Disclosed•Full-TimeOn-site
location_onGrace Street Commercial Historic District, Byrd Street Cycle Track, Richmond, Virginia, 23284, 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 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 continue our industry-leading capabilities with breakthrough product experiences and scalable, high-performance AI infrastructure.
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 the state of the art in AI to our business.
As a Senior Director, Applied Research, you will be a well-respected people leader guiding and mentoring a team of applied scientists. This role drives strategic direction through collaboration with Applied Science, Engineering, and Product leaders across Capital One. You will represent Capital One as an external leader in the research community, collaborating with prominent faculty members in the relevant AI research community.
In this position, you will partner with cross-functional teams to deliver AI-powered products that change how customers interact with their money. You will engage in high-impact applied research to take the latest AI developments and push them into the next generation of customer experiences. Your work will involve translating the complexity of your research into tangible business goals, ensuring that the transformative power of emerging AI capabilities is used to reimagine how we serve our customers and businesses.
Candidates hired to work in other locations will be subject to the pay range associated with that location. The actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate's offer letter. This role is expected to accept applications for a minimum of 5 business days.
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 to apply for a position, please contact Capital One Recruiting at 1-800-304-9102 or via email at RecruitingAccommodation@capitalone.com. All information provided will be kept confidential and used only to the extent required to provide needed reasonable accommodations.
Work model: On-site
Grace Street Commercial Historic District, Byrd Street Cycle Track, Richmond, Virginia, 23284, United States
Richmond, Virginia
PhD in Computer Science, Machine Learning, Computer Engineering, Applied Mathematics, or Electrical Engineering. LLM focus on NLP (PhD or Masters with 10 years industrial NLP research experience), core contributor to training a large language model from scratch (10B+ parameters, 500B+ tokens), numerous publications at ACL, NAACL, EMNLP, Neurips, ICML, or ICLR on LLM pre-training topics, experience with open source or commercial LLMs, ability to guide technical direction of large-scale model training teams, experience with 500+ node GPU clusters, LLM scaled to 70B parameters and 1T+ tokens, experience with training optimization frameworks (DeepSpeed, NeMo). Behavioral Models focus on geometric deep learning (Graph Neural Networks, Sequential Models, Multivariate Time Series), technical leadership for model deployment of large user behavior models, multiple papers on graph/sequential data training at KDD, ICML, NeurIPS, ICLR, scaling graph models to 50m+ nodes, experience with large-scale deep learning recommender systems, production real-time and streaming environments, contributions to open source frameworks (PyTorch Geometric, DGL), proposed new methods for inference or representation learning on graphs/sequences, experience with datasets of 100m+ users. Optimization focus on training very large language models, 5+ years experience or publications on model sparsification, quantization, training parallelism/partitioning design, gradient checkpointing, or model compression. Finetuning focus on guiding LLMs (Supervised Finetuning, Instruction-Tuning, Dialogue-Finetuning, Parameter Tuning), knowledge of transfer learning, model adaptation, and model guidance, experience deploying fine-tuned LLMs. Data Preparation focus on tokenization, data quality, dataset curation, or labeling, leading contributions to large open source corpora (1 Trillion+ tokens), core contributor to open source libraries for data quality, dataset curation, or labeling.
Capital One • New York, New York
Capital One • New York, New York
Capital One • New York, New York
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; Master's in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics or related fields.