This role pays more than
88%
of similar roles. Most pay
$189,525–$254,750
— the shaded band above.
At the midpoint, this role pays about
$281k
versus about
$222k
for comparable roles.
Based on 240 similar postings.
Employer
About Capital One Financial
Capital One Financial is a bank holding company specializing in credit cards, auto loans, banking, and savings products, known for its data-driven approach to consumer and commercial finance. Industry: Financial Services & Banking
Capital One Financial currently has
878 open roles
on FindRole.
Listed pay typically runs
$197,300–$225,100
across 872 roles with salary data.
The Applied Researcher 5 joins the AI Foundations team to develop trustworthy and reliable artificial intelligence systems for banking applications. Working within a cross-functional team of data scientists, software engineers, and product managers, you will build AI foundation models through every phase of development, including design, training, evaluation, validation, and implementation. You will leverage a technical stack featuring PyTorch, AWS Ultraclusters, Huggingface, and Lightning to extract insights from massive volumes of numeric and textual data. The role involves conducting high-impact applied research to translate state-of-the-art AI developments into production-ready customer experiences. Key competencies include expertise in large deep learning models, training optimization, self-supervised learning, and RLHF. You will solve complex problems by bridging the gap between prototype model development and scalable deployment to improve how customers interact with their money.
Build AI foundation models through all phases of development including design, training, evaluation, and implementation.
Conduct high-impact applied research to integrate state-of-the-art AI developments into customer experience products.
Analyze large volumes of numeric and textual data using tools like PyTorch, Hugging Face, and AWS Ultraclusters.
Lead cross-functional research threads to bridge the gap between prototype model development and production deployment.
Translate complex technical research findings into tangible business goals for stakeholders and product managers.
Research and evaluate emerging technologies to identify opportunities for improving existing machine learning systems.
Develop scalable models and high-quality code for large-scale training data and inference volumes.
Own and pursue a research agenda by selecting impactful problems and executing long-running projects independently.
What we're looking for
Must have a PhD in a relevant field (Engineering, CS, AI, Math) or an MS with 4 years of experience in Applied Research.
Must have a PhD in a related field plus 2 years of experience in Applied Research if the degree is still in progress.
Experience building large deep learning models and expertise in areas like training optimization, self-supervised learning, robustness, explainability, or RLHF.
Proven track record of delivering models at scale for both training data and inference volumes.
Experience delivering libraries, platform-level code, or solution-level code to existing products.
Demonstrated ability to own a research agenda, including selecting impactful problems and executing long-running projects independently.
Track record of high-quality machine learning contributions, such as first-author publications or significant project improvements.
Experience with large-scale production environments, open-source tools, and cloud computing platforms (preferred).