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About the Role

At Capital One, we are creating trustworthy and reliable AI systems to change banking for good. We are committed to building world-class applied science and engineering teams and continue our industry leading capabilities with breakthrough product experiences and scalable, high-performance AI infrastructure. You will help bring the transformative power of emerging AI capabilities to reimagine how we serve our customers and businesses.

Responsibilities

Partner with a cross-functional team of data scientists, software engineers, machine learning engineers and product managers to deliver AI-powered products.
Leverage a broad stack of technologies — Pytorch, AWS Ultraclusters, Huggingface, Lightning, VectorDBs, and more — to reveal insights within data.
Build AI foundation models through all phases of development, from design through training, evaluation, validation, and implementation.
Engage in high impact applied research to take the latest AI developments and push them into the next generation of customer experiences.
Flex interpersonal skills to translate the complexity of work into tangible business goals.

Requirements

Currently have, or be in the process of obtaining, PhD in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields, with an exception that required degree will be obtained on or before the scheduled start date plus 2 years of experience in Applied Research or M.S. in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields plus 4 years of experience in Applied Research
PhD focus on NLP or Masters with 5 years of industrial NLP research experience
Multiple publications on topics related to the pre-training of large language models (e.g. technical reports of pre-trained LLMs, SSL techniques, model pre-training optimization)
Member of team that has trained a large language model from scratch (10B + parameters, 500B+ tokens)
Publications in deep learning theory
Publications at ACL, NAACL and EMNLP, Neurips, ICML or ICLR
PhD focus on topics in geometric deep learning (Graph Neural Networks, Sequential Models, Multivariate Time Series)
Multiple papers on topics relevant to training models on graph and sequential data structures at KDD, ICML, NeurIPs, ICLR
Worked on 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 common open source frameworks (pytorch-geometric, DGL)
Proposed new methods for inference or representation learning on graphs or sequences
Worked datasets with 100m+ users
PhD focused on topics related to optimizing training of very large deep learning models
Multiple years of experience and/or publications on one of the following topics: Model Sparsification, Quantization, Training Parallelism/Partitioning Design, Gradient Checkpointing, 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 topics related to 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 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
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.
Experience building large deep learning models, whether on language, images, events, or graphs, as well as expertise in one or more of the following: training optimization, self-supervised learning, robustness, explainability, RLHF.
An engineering mindset as shown by a track record of delivering models at scale both in terms of training data and inference volumes.
Experience in delivering libraries, platform level code or solution level code to existing products.
A track record of coming up with new ideas or improving upon existing ideas in machine learning, demonstrated by accomplishments such as first author publications or projects.
Ability to own and pursue a research agenda, including choosing impactful research problems and autonomously carrying out long-running projects.

Skills

Pytorch
AWS Ultraclusters
Huggingface
Lightning
VectorDBs
LLM
NLP
deep learning theory
geometric deep learning
Graph Neural Networks
Sequential Models
Multivariate Time Series
Model Sparsification
Quantization
Training Parallelism/Partitioning Design
Gradient Checkpointing
Model Compression
Supervised Finetuning
Instruction-Tuning
Dialogue-Finetuning
Parameter Tuning
transfer learning
model adaptation
model guidance
tokenization
data quality
dataset curation
labeling

Location

McLean, VA
New York, NY
Cambridge, MA
San Francisco, CA
San Jose, CA

Work Type

full-time

Experience Level

Applied Researcher II

Education Level

PhD in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields
M.S. in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields
PhD in Computer Science, Machine Learning, Computer Engineering, Applied Mathematics, Electrical Engineering or related fields

Salary/Compensations

McLean, VA: $262,500 - $299,600
New York, NY: $286,400 - $326,800
Cambridge, MA: $262,500 - $299,600
San Francisco, CA: $286,400 - $326,800
San Jose, CA: $286,400 - $326,800

Benefits

Comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being.

About the Company

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 work with product, technology and business leaders to apply the state of the art in AI to our business.

Equal Opportunity

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.
Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections 4901-4920; New York City’s Fair Chance Act; Philadelphia’s Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries.