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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 to reveal insights within large volumes of numeric and textual 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 are in the process of obtaining, a 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 or M.S. in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields plus 2 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.
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 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 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.

Skills

Pytorch
AWS Ultraclusters
Huggingface
Lightning
VectorDBs
AI foundation models
Machine learning
Deep learning
NLP
LLM
Geometric deep learning
Graph Neural Networks
Sequential Models
Multivariate Time Series
Recommender systems
Model Sparsification
Quantization
Training Parallelism/Partitioning Design
Gradient Checkpointing
Model Compression
Deep learning algorithmic design
Optimizer design
Compiler design
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 I
PhD
M.S. plus 2 years of experience
5 years of industrial NLP research experience
Multiple years of experience

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: $218,700 - $249,600
New York, NY: $238,600 - $272,300
Cambridge, MA: $218,700 - $249,600
San Francisco, CA: $238,600 - $272,300
San Jose, CA: $238,600 - $272,300

Benefits

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

About the Company

At Capital One, we are creating trustworthy and reliable AI systems, changing banking for good.
For years, Capital One has been leading the industry in using machine learning to create real-time, intelligent, automated customer experiences.
Our applications of AI & ML are bringing humanity and simplicity to banking.
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.
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.