Capital One
Overview
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. From informing customers about unusual charges to answering their questions in real time, 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. 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.
Team Description 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.
In this role
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, VectorDBs 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 your interpersonal skills to translate the complexity of your work into tangible business goals.
The Ideal Candidate
You love the process of analyzing and creating, but also share our passion to do the right thing. You know at the end of the day it’s about making the right decision for our customers.
Innovative. You continually research and evaluate emerging technologies. You stay current on published state‑of‑the‑art methods, technologies, and applications and seek out opportunities to apply them.
Creative. You thrive on bringing definition to big, undefined problems. You love asking questions and pushing hard to find answers. You’re not afraid to share a new idea.
A leader. You challenge conventional thinking and work with stakeholders to identify and improve the status quo. You’re passionate about talent development for your own team and beyond.
Technical. You’re comfortable with open‑source languages and are passionate about developing further. You have hands‑on experience developing AI foundation models and solutions using open‑source tools and cloud computing platforms.
Has a 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 professional with 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.
Possess the ability to own and pursue a research agenda, including choosing impactful research problems and autonomously carrying out long‑running projects.
Key Responsibilities
Partner with a cross‑functional team of scientists, machine learning engineers, software engineers, and product managers to deliver AI‑powered platforms and solutions that change how customers interact with their money.
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.
Leverage a broad stack of technologies – Pytorch, AWS Ultraclusters, Huggingface, Lightning, VectorDBs and more – to reveal the insights hidden within huge volumes of numeric and textual data.
Flex your interpersonal skills to translate the complexity of your work into tangible business goals.
Basic Qualifications PhD in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields plus 4 years of experience in Applied Research, or M.S. in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields plus 6 years of experience in Applied Research.
Preferred Qualifications
PhD in Computer Science, Machine Learning, Computer Engineering, Applied Mathematics, Electrical Engineering or related fields.
LLM.
PhD focus on NLP or Masters with 10 years of industrial NLP research experience.
Core contributor to team that has trained a large language model from scratch (10B+ parameters, 500B+ tokens) or through continued pre‑training, post‑training pipeline for alignment and reasoning, LLM optimizations, complex reasoning with multi‑agentic LLMs.
Numerous publications at ACL, NAACL and EMNLP, NeurIPS, ICML or ICLR 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).
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 common training optimization frameworks (deep speed, nemo).
Experience contributing to the team that has trained a large language model from scratch (10B+ parameters, 500B+ tokens) or through continued pre‑training, post‑training pipeline for alignment and reasoning, LLM optimizations, complex reasoning with multi‑agentic LLMs.
Compensation Minimum and maximum full‑time annual salaries for this role are listed below, by location:
Sales Territory: $273,000 – $311,500
Cambridge, MA: $300,200 – $342,600
McLean, VA: $300,200 – $342,600
New York, NY: $327,600 – $373,800
San Francisco, CA: $327,600 – $373,800
Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate’s offer letter.
Benefits Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well‑being. Learn more at the Capital One Careers website. Eligibility varies based on full or part‑time status, exempt or non‑exempt status, and management level.
EEO Statement 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.
Contact Recruiting Accommodation: RecruitingAccommodation@capitalone.com. For technical support or questions about the recruiting process, send an email to Careers@capitalone.com.
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Team Description 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.
In this role
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, VectorDBs 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 your interpersonal skills to translate the complexity of your work into tangible business goals.
The Ideal Candidate
You love the process of analyzing and creating, but also share our passion to do the right thing. You know at the end of the day it’s about making the right decision for our customers.
Innovative. You continually research and evaluate emerging technologies. You stay current on published state‑of‑the‑art methods, technologies, and applications and seek out opportunities to apply them.
Creative. You thrive on bringing definition to big, undefined problems. You love asking questions and pushing hard to find answers. You’re not afraid to share a new idea.
A leader. You challenge conventional thinking and work with stakeholders to identify and improve the status quo. You’re passionate about talent development for your own team and beyond.
Technical. You’re comfortable with open‑source languages and are passionate about developing further. You have hands‑on experience developing AI foundation models and solutions using open‑source tools and cloud computing platforms.
Has a 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 professional with 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.
Possess the ability to own and pursue a research agenda, including choosing impactful research problems and autonomously carrying out long‑running projects.
Key Responsibilities
Partner with a cross‑functional team of scientists, machine learning engineers, software engineers, and product managers to deliver AI‑powered platforms and solutions that change how customers interact with their money.
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.
Leverage a broad stack of technologies – Pytorch, AWS Ultraclusters, Huggingface, Lightning, VectorDBs and more – to reveal the insights hidden within huge volumes of numeric and textual data.
Flex your interpersonal skills to translate the complexity of your work into tangible business goals.
Basic Qualifications PhD in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields plus 4 years of experience in Applied Research, or M.S. in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields plus 6 years of experience in Applied Research.
Preferred Qualifications
PhD in Computer Science, Machine Learning, Computer Engineering, Applied Mathematics, Electrical Engineering or related fields.
LLM.
PhD focus on NLP or Masters with 10 years of industrial NLP research experience.
Core contributor to team that has trained a large language model from scratch (10B+ parameters, 500B+ tokens) or through continued pre‑training, post‑training pipeline for alignment and reasoning, LLM optimizations, complex reasoning with multi‑agentic LLMs.
Numerous publications at ACL, NAACL and EMNLP, NeurIPS, ICML or ICLR 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).
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 common training optimization frameworks (deep speed, nemo).
Experience contributing to the team that has trained a large language model from scratch (10B+ parameters, 500B+ tokens) or through continued pre‑training, post‑training pipeline for alignment and reasoning, LLM optimizations, complex reasoning with multi‑agentic LLMs.
Compensation Minimum and maximum full‑time annual salaries for this role are listed below, by location:
Sales Territory: $273,000 – $311,500
Cambridge, MA: $300,200 – $342,600
McLean, VA: $300,200 – $342,600
New York, NY: $327,600 – $373,800
San Francisco, CA: $327,600 – $373,800
Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate’s offer letter.
Benefits Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well‑being. Learn more at the Capital One Careers website. Eligibility varies based on full or part‑time status, exempt or non‑exempt status, and management level.
EEO Statement 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.
Contact Recruiting Accommodation: RecruitingAccommodation@capitalone.com. For technical support or questions about the recruiting process, send an email to Careers@capitalone.com.
#J-18808-Ljbffr