Senior Director, Applied Research
Hobbsnews, San Francisco, CA, United States
Senior Director, Applied Research
Overview
At Capital One, we are creating trustworthy and reliable AI systems, changing banking for good. We lead 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 AI and ML applications bring humanity and simplicity to banking and support world‑class applied science and engineering teams that deliver breakthrough products and scalable, high‑performance AI infrastructure. In this role, you will help bring the transformative power of emerging AI capabilities to reimagine how we serve our customers and businesses.
Team Description
The AI Foundations team is at the center of bringing our vision for AI at Capital One to life. Our work spans 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.
Role Overview
This is a people manager role that will lead teams to drive strategic direction through collaboration with Applied Science, Engineering, and Product leaders across Capital One. As a well‑respected people leader, you will guide and mentor a team of applied scientists and represent Capital One in the research community, collaborating with prominent faculty members in the relevant AI research community.
Responsibilities
Partner with a cross‑functional team of data scientists, software engineers, machine learning engineers, and product managers to deliver AI‑powered products that change how customers interact with their money.
Leverage a broad stack of technologies—including Pytorch, AWS Ultraclusters, Huggingface, Lightning, VectorDBs, and more—to reveal insights hidden within huge volumes of numeric and textual data.
Build AI foundation models through all phases of development: design, training, evaluation, validation, and implementation.
Engage in high‑impact applied research to translate the latest AI developments into next‑generation customer experiences.
Translate the complexity of your work into tangible business goals and collaborate with stakeholders to prioritize initiatives.
Ideal Candidate
Love the process of analyzing and creating, and share our passion to do the right thing for customers.
Innovative and continually research and evaluate emerging technologies, staying current on state‑of‑the‑art methods and applications.
Creative, able to bring definition to big, undefined problems and share new ideas confidently.
Leader who challenges conventional thinking, works with stakeholders to improve the status quo, and is passionate about talent development for the team and beyond.
Technical, comfortable with open‑source languages, and passionate about developing further; hands‑on experience developing AI foundation models and solutions using open‑source tools and cloud computing platforms.
Deep understanding of the foundations of AI methodologies and experience building large deep learning models across language, images, events, or graphs.
Engineering mindset demonstrated by delivering models at scale, both in terms of training data and inference volumes.
Experience delivering libraries, platform‑level or solution‑level code to existing products.
Track record of innovating in machine learning, evidenced by first‑author publications or impactful projects.
Ability to own and pursue a research agenda, choosing impactful problems and autonomously carrying out long‑running projects.
Basic Qualifications
PhD in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields plus 6 years of experience in Applied Research, or M.S. in the same fields plus 8 years of experience in Applied Research.
At least 5 years of people leadership experience.
Preferred Qualifications
PhD in Computer Science, Machine Learning, Computer Engineering, Applied Mathematics, Electrical Engineering, or related fields.
LLM
PhD focus on NLP or Master’s with 10 years of industrial NLP research experience.
Core contributor to a team that has trained a large language model from scratch (10 B+ parameters, 500 B+ tokens).
Numerous publications at ACL, NAACL, EMNLP, NeurIPS, ICML, or ICLR on topics related to pre‑training of large language models.
Has worked on an LLM (open source or commercial) that is currently available for use.
Demonstrated ability to guide the technical direction of a large‑scale model training team.
Experience with 500+ node GPU clusters and scaling LLMs to 70 B parameters and 1 T+ tokens.
Experience with common training optimization frameworks (DeepSpeed, NeMo).
Behavioral Models
PhD focus on geometric deep learning (Graph Neural Networks, Sequential Models, Multivariate Time Series).
Member of technical leadership for deployment of a very large user‑behavior model.
Publications at KDD, ICML, NeurIPS, or ICLR on training models on graph and sequential data structures.
Experience scaling graph models to >50 M nodes and large‑scale deep‑learning 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.
Worked with datasets of >100 M users.
Optimization (Training & Inference)
PhD focused on optimizing training of very large language models.
5+ years of experience and/or publications on model sparsification, quantization, training parallelism/partitioning design, gradient checkpointing, or model compression.
Finetuning
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 finetuned large language model.
Data Preparation
Numerous publications on tokenization, data quality, dataset curation, or labeling.
Leading contributions to one or more large open‑source corpora (1 trillion+ tokens).
Core contributor to open‑source libraries for data quality, dataset curation, or labeling.
Capital One will consider sponsoring a new qualified applicant for employment authorization for this position.
Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non‑discrimination in compliance with applicable federal, state, and local laws.
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Overview
At Capital One, we are creating trustworthy and reliable AI systems, changing banking for good. We lead 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 AI and ML applications bring humanity and simplicity to banking and support world‑class applied science and engineering teams that deliver breakthrough products and scalable, high‑performance AI infrastructure. In this role, you will help bring the transformative power of emerging AI capabilities to reimagine how we serve our customers and businesses.
Team Description
The AI Foundations team is at the center of bringing our vision for AI at Capital One to life. Our work spans 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.
Role Overview
This is a people manager role that will lead teams to drive strategic direction through collaboration with Applied Science, Engineering, and Product leaders across Capital One. As a well‑respected people leader, you will guide and mentor a team of applied scientists and represent Capital One in the research community, collaborating with prominent faculty members in the relevant AI research community.
Responsibilities
Partner with a cross‑functional team of data scientists, software engineers, machine learning engineers, and product managers to deliver AI‑powered products that change how customers interact with their money.
Leverage a broad stack of technologies—including Pytorch, AWS Ultraclusters, Huggingface, Lightning, VectorDBs, and more—to reveal insights hidden within huge volumes of numeric and textual data.
Build AI foundation models through all phases of development: design, training, evaluation, validation, and implementation.
Engage in high‑impact applied research to translate the latest AI developments into next‑generation customer experiences.
Translate the complexity of your work into tangible business goals and collaborate with stakeholders to prioritize initiatives.
Ideal Candidate
Love the process of analyzing and creating, and share our passion to do the right thing for customers.
Innovative and continually research and evaluate emerging technologies, staying current on state‑of‑the‑art methods and applications.
Creative, able to bring definition to big, undefined problems and share new ideas confidently.
Leader who challenges conventional thinking, works with stakeholders to improve the status quo, and is passionate about talent development for the team and beyond.
Technical, comfortable with open‑source languages, and passionate about developing further; hands‑on experience developing AI foundation models and solutions using open‑source tools and cloud computing platforms.
Deep understanding of the foundations of AI methodologies and experience building large deep learning models across language, images, events, or graphs.
Engineering mindset demonstrated by delivering models at scale, both in terms of training data and inference volumes.
Experience delivering libraries, platform‑level or solution‑level code to existing products.
Track record of innovating in machine learning, evidenced by first‑author publications or impactful projects.
Ability to own and pursue a research agenda, choosing impactful problems and autonomously carrying out long‑running projects.
Basic Qualifications
PhD in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields plus 6 years of experience in Applied Research, or M.S. in the same fields plus 8 years of experience in Applied Research.
At least 5 years of people leadership experience.
Preferred Qualifications
PhD in Computer Science, Machine Learning, Computer Engineering, Applied Mathematics, Electrical Engineering, or related fields.
LLM
PhD focus on NLP or Master’s with 10 years of industrial NLP research experience.
Core contributor to a team that has trained a large language model from scratch (10 B+ parameters, 500 B+ tokens).
Numerous publications at ACL, NAACL, EMNLP, NeurIPS, ICML, or ICLR on topics related to pre‑training of large language models.
Has worked on an LLM (open source or commercial) that is currently available for use.
Demonstrated ability to guide the technical direction of a large‑scale model training team.
Experience with 500+ node GPU clusters and scaling LLMs to 70 B parameters and 1 T+ tokens.
Experience with common training optimization frameworks (DeepSpeed, NeMo).
Behavioral Models
PhD focus on geometric deep learning (Graph Neural Networks, Sequential Models, Multivariate Time Series).
Member of technical leadership for deployment of a very large user‑behavior model.
Publications at KDD, ICML, NeurIPS, or ICLR on training models on graph and sequential data structures.
Experience scaling graph models to >50 M nodes and large‑scale deep‑learning 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.
Worked with datasets of >100 M users.
Optimization (Training & Inference)
PhD focused on optimizing training of very large language models.
5+ years of experience and/or publications on model sparsification, quantization, training parallelism/partitioning design, gradient checkpointing, or model compression.
Finetuning
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 finetuned large language model.
Data Preparation
Numerous publications on tokenization, data quality, dataset curation, or labeling.
Leading contributions to one or more large open‑source corpora (1 trillion+ tokens).
Core contributor to open‑source libraries for data quality, dataset curation, or labeling.
Capital One will consider sponsoring a new qualified applicant for employment authorization for this position.
Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non‑discrimination in compliance with applicable federal, state, and local laws.
#J-18808-Ljbffr