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About the Role
We are creating trustworthy and reliable AI systems to change banking for good. Our applications of AI & ML bring humanity and simplicity to banking, and we are committed to building world-class applied science and engineering teams. 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 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 pushing hard 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 the 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.
Skills
- Pytorch
- AWS Ultraclusters
- Huggingface
- Lightning
- Large deep learning models
- Training optimization
- Self-supervised learning
- Robustness
- Explainability
- RLHF
- LLM
- NLP
- 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
- 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
- Masters
Salary/Compensations
- Cambridge, MA: $218,700 - $249,600
- McLean, VA: $218,700 - $249,600
- New York, NY: $238,600 - $272,300
- 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.
- 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.