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About the Role

At Capital One, we are creating trustworthy and reliable AI systems, changing 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. At Capital One, you will help bring the transformative power of emerging AI capabilities to reimagine how we serve our customers and businesses who have come to love the products and services we build. 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.

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 — Pytorch, AWS Ultraclusters, Huggingface, Lightning, and more — to reveal the 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.
Flex interpersonal skills to translate the complexity of work into tangible business goals.
Collaborate with senior researchers to prepare internal tech reports or conference submissions summarizing novel methods or findings.

Requirements

Innovative: Continually research and evaluate emerging technologies and stay current on published state-of-the-art methods, technologies, and applications.
Creative: Thrive on bringing definition to big, undefined problems and asking questions to find answers.
Leader: Challenge conventional thinking and work with stakeholders to identify and improve the status quo.
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.
Experience building large deep learning models (language, images, events, or graphs).
Expertise in one or more of the following: training optimization, self-supervised learning, robustness, explainability, RLHF.
Engineering mindset with a track record of delivering models at scale (training data and inference volumes).
Experience in delivering libraries, platform level code or solution level code to existing products.
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.
Ability to own and pursue a research agenda, including choosing impactful research problems and autonomously carrying out long-running projects.
Currently have, or be 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 in Computer Science, Machine Learning, Computer Engineering, Applied Mathematics, Electrical Engineering or related fields.
LLM experience.
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 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.
Demonstrated ability to reproduce and extend peer-reviewed AI research using modern open-source frameworks.
Experience designing controlled experiments and documenting reproducibility results for internal or public research.

Skills

Pytorch
AWS Ultraclusters
Huggingface
Lightning
AI
ML
Deep Learning
NLP
LLM
Optimization
Finetuning
Data Preparation
Open-source tools
Cloud computing platforms

Location

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

Work Type

Full-time

Experience Level

Applied Researcher 4
2 years of experience in Applied Research (with M.S.)
5 years of industrial NLP research experience (with Masters)

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

$218,700 - $249,600
$238,600 - $272,300

Benefits

Comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being.
Eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI).

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.
Our applications of AI & ML are bringing humanity and simplicity to banking.
Capital One Financial is made up of several different entities.

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.
If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1-800-304-9102 or via email at RecruitingAccommodation@capitalone.com. All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations.