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About the Role

At Capital One, we are creating trustworthy and reliable AI systems, changing banking for good. This individual contributor role drives strategic direction through collaboration with Applied Science, Engineering, and Product leaders across Capital One. You will guide and mentor a team of applied scientists and their managers without being a direct people leader, and represent Capital One externally in the 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.
Leverage a broad stack of technologies to reveal insights hidden within huge 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.
Translate the complexity of your work into tangible business goals.
Define and steward novel research directions that expand the organization’s long-term scientific agenda, ensuring foundational discoveries translate into deployable capabilities.

Requirements

Innovative: Continually research and evaluate emerging technologies and stay current on published state-of-the-art methods.
Creative: Thrive on bringing definition to big, undefined problems and sharing new ideas.
Leader: Challenge conventional thinking and work with stakeholders to identify and improve the status quo.
Technical: Comfortable with open-source languages and have 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.
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 professional with a track record of coming up with high quality ideas or improving upon existing ideas in machine learning, demonstrated by accomplishments such as first author publications or projects.
Possess the 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
LLM
NLP
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
Tokenization
Data quality
Dataset curation
Labeling

Location

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

Work Type

Remote
Hybrid

Experience Level

Staff Applied Researcher

Education Level

PhD in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields plus 4 years of experience in Applied Research
M.S. in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields plus 6 years of experience in Applied Research
PhD in Computer Science, Machine Learning, Computer Engineering, Applied Mathematics, Electrical Engineering or related fields
PhD focus on NLP or Masters with 10 years of industrial NLP research experience
PhD focus on topics in geometric deep learning (Graph Neural Networks, Sequential Models, Multivariate Time Series)
PhD focused on topics related to optimizing training of very large language models
5+ years of experience and/or publications on one of the following topics: Model Sparsification, Quantization, Training Parallelism/Partitioning Design, Gradient Checkpointing, Model Compression
PhD focused on topics related to guiding LLMs with further tasks (Supervised Finetuning, Instruction-Tuning, Dialogue-Finetuning, Parameter Tuning)
Numerous Publications studying tokenization, data quality, dataset curation, or labeling
Leading contributions to one or more large open source corpus (1 Trillion + tokens)
Core contributor to open source libraries for data quality, dataset curation, or labeling
Demonstrated history of initiating and leading multi-year research programs that shaped the direction of AI or ML subfields (e.g., representation learning, foundation models, multimodal reasoning)

Salary/Compensations

Cambridge, MA: $306,300 - $349,500
McLean, VA: $306,300 - $349,500
New York, NY: $334,100 - $381,300
San Francisco, CA: $334,100 - $381,300
San Jose, CA: $334,100 - $381,300

Benefits

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

About the Company

Capital One is 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.
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.