Applied Researcher I – AI Foundations, LLM Customization, Finetuning, Reinforcement Learning

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

Job summary

Capital One is seeking a senior AI researcher to partner with data scientists, software engineers, ML engineers, and product managers to deliver AI-powered products.

You will build AI foundation models through design, training, evaluation, validation, and implementation, using PyTorch, AWS Ultraclusters, Hugging Face, and other tools to translate research into business outcomes for banking products.

Qualifications

• Pursuing or holding a PhD in a related technical field.
• Deep understanding of AI foundations and methodologies.
• Experience building large deep learning models for language, images, events, or graphs.
• Expertise in training optimization, self-supervised learning, robustness, explainability, or RLHF.
• Proven ability to deliver models at scale in training data and inference volumes.
• Experience delivering libraries or platform-level code for products.
• Strong track record of impactful ML research (publications or notable projects).
• Ability to own a research agenda and drive long-running projects autonomously.
• Hands-on AI foundation model development using open-source tools and cloud platforms.

Responsibilities

• Partner with data scientists, software engineers, ML engineers, and PMs to deliver AI-powered products.
• Leverage PyTorch, AWS Ultraclusters, Hugging Face, Lightning, VectorDBs and other tech to analyze data.
• Build AI foundation models through design, training, evaluation, validation, and implementation.
• Conduct applied research to advance AI capabilities and customer experiences.
• Translate complex research into tangible business goals.
• Apply state-of-the-art AI research to Capital One's banking products and services.

Skills

Deep learning

AI methodologies

Model Training

Model Evaluation

Applied Research

NLP

Supervised Finetuning

Instruction-Tuning

Dialogue-Finetuning

Parameter Tuning

Education

PhD in Electrical Engineering

PhD in Computer Engineering

PhD in Computer Science

PhD in AI

Mathematics or related field

MS + 2 years Applied Research

Tools

PyTorch

AWS Ultraclusters

Hugging Face

Lightning

VectorDBs

Open-Source Tools

Cloud Computing Platforms

Job description

• Partner with data scientists, software engineers, machine learning engineers, and product managers to deliver AI-powered products
• Leverage PyTorch, AWS Ultraclusters, Hugging Face, Lightning, VectorDBs, and other technologies to analyze numeric and textual data
• Build AI foundation models through design, training, evaluation, validation, and implementation
• Conduct applied research to advance AI capabilities and customer experiences
• Translate complex research into tangible business goals
• Apply state-of-the-art AI research to Capital One's banking products and services

Requirements

• Currently has, or is 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; or an M.S. in those fields 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 high-quality ideas or improvements in machine learning, 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 a related technical field
• Preferred: PhD focus on NLP or Master's with 5 years of industrial NLP research experience
• Preferred: publications related to pre-training large language models, deep learning theory, or major machine learning conferences
• Preferred: experience training a large language model from scratch with 10B+ parameters and 500B+ tokens
• Preferred: experience with supervised finetuning, instruction-tuning, dialogue-finetuning, or parameter tuning
• Preferred: knowledge of transfer learning, model adaptation, and model guidance
• Preferred: experience deploying a fine-tuned large language model
• Preferred: publications or open-source contributions related to tokenization, data quality, dataset curation, or labeling
• Capital One will consider sponsoring a new qualified applicant for employment authorization

Core Competencies

Demonstrates expertise in building and deploying AI foundation models, with a strong focus on deep learning methodologies and applied research. Proven ability to translate complex AI concepts into actionable business strategies while collaborating with cross-functional teams.

Highest-signal resume keywords

• PhD In Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics
• Experience Building Large Deep Learning Models
• Expertise In Training Optimization, Self-Supervised Learning, Robustness, Explainability, RLHF
• Hands-On Experience Developing AI Foundation Models Using Open-Source Tools
• Track Record Of Delivering Models At Scale

Hard Skills

• Deep Learning
• AI Methodologies
• Model Training
• Model Evaluation
• Applied Research
• Natural Language Processing (NLP)
• Supervised Finetuning
• Instruction-Tuning
• Dialogue-Finetuning
• Parameter Tuning

Soft Skills

• Collaboration
• Autonomous Project ManagementProblem-Solving

Industry Keywords

• AI-Powered Products
• Machine Learning
• Large Language Models
• Data Quality
• Dataset Curation

Tools & Technologies

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