Applied Researcher II – AI Foundations

Jobtailor; California (MO); On-site; USD 150,000 - 230,000; Full time

Job summary

Capital One seeks a research scientist to partner with data scientists, software engineers, ML engineers, and product managers to deliver AI-powered banking products. You will build AI foundation models through design, training, evaluation, validation, and implementation.

Responsibilities include translating complex research into tangible business goals and collaborating with product and business leaders to apply state-of-the-art AI in banking. PhD or MS with strong research background required.

Qualifications

• PhD in Electrical Engineering, Computer Engineering, CS, AI, Mathematics or related field with degree obtained by start date.
• MS in related field plus 4 years of applied research experience or PhD with 2 years of applied research.
• Deep understanding of AI foundations and large DL model development.
• Experience training large models for language, images, events, or graphs.
• Expertise in training optimization, self-supervised learning, robustness, explainability, or RLHF.
• Track record delivering scalable models and libraries to products.
• Publications or projects demonstrating a research agenda and autonomous long-running work.
• Hands-on experience with AI foundation models using open-source tools and cloud platforms.

Responsibilities

• Partner with data scientists, software engineers, ML engineers, and PMs to deliver AI-powered products.
• Use PyTorch, AWS Ultraclusters, Hugging Face, Lightning, VectorDBs to analyze large data volumes.
• Build AI foundation models through design, training, evaluation, validation, and implementation.
• Translate complex research into tangible business goals and outcomes.
• Collaborate with product, technology, and business leaders to apply AI in banking.

Skills

Deep Learning

PyTorch

RLHF

Self-Supervised Learning

Model Training

Explainability

Education

PhD in Electrical Engineering

MS in Electrical Engineering / Computer Science / AI

Tools

Hugging Face

AWS Ultraclusters

Lightning

VectorDBs

Open-Source Tools

Job description

Partner with data scientists, software engineers, machine-learning engineers, and product managers to deliver AI-powered products
Use PyTorch, AWS Ultraclusters, Hugging Face, Lightning, VectorDBs, and other technologies to analyze large volumes of numeric and textual data
Build AI foundation models through design, training, evaluation, validation, and implementation
Conduct applied research and apply emerging AI developments to customer experiences
Translate complex research and technical work into tangible business goals
Collaborate with product, technology, and business leaders to apply state-of-the‑art AI to banking

Requirements

• Currently has, or is in the process of obtaining, PhD in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields, with the required degree obtained on or before the scheduled start date
• Alternatively, M.S. in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields plus 4 years of experience in Applied Research
• Alternatively, PhD path plus 2 years of experience in Applied Research
• Deep understanding of the foundations of AI methodologies
• Experience building large deep learning models for language, images, events, or graphs
• Expertise in one or more of training optimization, self-supervised learning, robustness, explainability, or RLHF
• Track record of delivering models at scale in training data and inference volumes
• Experience delivering libraries, platform-level code, or solution-level code to existing products
• Track record of new or improved machine-learning ideas, demonstrated by accomplishments such as first-author publications or projects
• Ability to own and pursue a research agenda, select impactful research problems, and autonomously carry out long-running projects
• Hands-on experience developing AI foundation models and solutions using open-source tools and cloud-computing platforms
• Preferred: PhD in Computer Science, Machine Learning, Computer Engineering, Applied Mathematics, Electrical Engineering, or related fields
• Preferred: expertise in geometric deep learning, graph neural networks, sequential models, multivariate time series, recommender systems, real-time and streaming environments, fine-tuned large language models, transfer learning, model adaptation, tokenization, data quality, dataset curation, or labeling
• Preferred: publications at KDD, ICML, NeurIPS, or ICLR; experience scaling graph models beyond 50m nodes or working with datasets with 100m+ users; contributions to PyTorch Geometric, DGL, or other open-source frameworks/corpora
• Capital One will consider sponsoring employment authorization for a new qualified applicant

Core Competencies

Demonstrates expertise in building and delivering AI foundation models, with a strong focus on deep learning methodologies and applied research. Proficient in utilizing advanced tools and technologies to translate complex AI concepts into practical business applications.

Highest-signal resume keywords

• PhD In Electrical Engineering
• Deep Learning Model Development
• Applied Research Experience
• Expertise In Training Optimization
• Hands-On Experience With PyTorch

ATS Optimization Keywords

Hard Skills

• AI Methodologies
• Deep Learning
• Model Training
• Self-Supervised Learning
• Robustness
• Explainability
• Reinforcement Learning From Human Feedback
• Data Analysis
• Model Evaluation
• Model Validation

Soft Skills

• Collaboration
• Communication
• Problem-Solving
• Autonomy
• Research Agenda Ownership

Industry Keywords

• AI-Powered Products
• Machine Learning
• Banking
• Applied Research
• Publications In KDD
• ICML
• NeurIPS
• ICLR

Tools & Technologies

• PyTorch
• AWS Ultraclusters
• Hugging Face
• Lightning
• VectorDBs
• Open-Source Tools
• Cloud-Computing Platforms