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About the Role
At Capital One, we are creating trustworthy and reliable AI systems to transform banking. We are committed to building world-class applied science and engineering teams to advance 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 including Pytorch, AWS Ultraclusters, Huggingface, and Lightning to analyze 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 advance AI developments and integrate them into customer experiences.
- Translate the complexity of your work into tangible business goals.
- Lead cross-functional research threads that bridge prototype model development and deployment with ML engineering partners.
- Own and pursue a research agenda, including choosing impactful research problems and autonomously carrying out long-running projects.
Requirements
- Innovative mindset with continuous research and evaluation of emerging technologies and state-of-the-art methods.
- Creative problem-solver who thrives on defining and solving big, undefined problems.
- Leadership qualities, challenging conventional thinking and improving the status quo.
- Technical proficiency with open-source languages and a passion for development.
- Hands-on experience developing AI foundation models and solutions using open-source tools and cloud computing platforms.
- Deep understanding 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 demonstrated by a track record of delivering models at scale.
- Experience in delivering libraries, platform level code, or solution level code to existing products.
- Track record of high-quality ideas or improvements in machine learning, evidenced by publications or 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 the degree obtained on or before the scheduled start date plus 2 years of experience in Applied Research.
- OR an M.S. in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields plus 4 years of experience in Applied Research.
Skills
- Pytorch
- AWS Ultraclusters
- Huggingface
- Lightning
- AI
- ML
- Deep Learning
- LLM
- NLP
- Training Optimization
- Self-supervised learning
- Robustness
- Explainability
- RLHF
- Model Sparsification
- Quantization
- Training Parallelism/Partitioning Design
- Gradient Checkpointing
- Model Compression
- Deep learning optimizer design
- Compiler design
- Supervised Finetuning
- Instruction-Tuning
- Dialogue-Finetuning
- Parameter Tuning
- Transfer learning
- Tokenization
- Data quality
- Dataset curation
- Labeling
Location
- Cambridge, MA
- McLean, VA
- New York, NY
- San Francisco, CA
- San Jose, CA
Work Type
- Full-time
Experience Level
- Applied Researcher 5
Education Level
- PhD in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields
- MS 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
- PhD focus on NLP
- Masters with 5 years of industrial NLP research experience
- PhD focused on topics related to optimizing training of very large deep learning models
- PhD focused on topics related to guiding LLMs with further tasks
Salary/Compensations
- Cambridge, MA: $262,500 - $299,600
- McLean, VA: $262,500 - $299,600
- New York, NY: $286,400 - $326,800
- 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.
- 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.
- Capital One has been leading the industry in using machine learning to create real-time, intelligent, automated customer experiences.
- Our applications of AI & ML bring humanity and simplicity to banking.
- 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.
- 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.