below marketmost similar roles pay hereabove market
This role pays more than
88%
of similar roles. Most pay
$211,200–$254,750
— the blue band above.
At the midpoint, this role pays about
$281k
versus about
$233k
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
2276 open roles
on FindRole.
Listed pay typically runs
$197,300–$225,100
across 1147 roles with salary data.
The Applied Researcher 5 (AI Foundations, LLM Core and Agentic AI) joins the AI Foundations team to develop trustworthy and reliable AI systems. This role involves partnering with cross-functional teams of data scientists, software engineers, and product managers to build AI-powered products and foundation models through all development phases, including design, training, evaluation, and implementation. You will leverage a technology stack including Pytorch, AWS Ultraclusters, Huggingface, and Lightning to extract insights from large volumes of numeric and textual data. Key responsibilities include leading research threads that bridge prototype development with production deployment, focusing on training optimization, self-supervised learning, robustness, explainability, and RLHF. The work addresses the technical challenge of translating state-of-the-art AI research into scalable, high-performance infrastructure for real-time, intelligent customer experiences.
Build AI foundation models through all phases of development including design, training, evaluation, and implementation.
Leverage Pytorch, AWS Ultraclusters, and Huggingface to extract insights from large volumes of numeric and textual data.
Conduct high-impact applied research to integrate the latest AI developments into next-generation customer experiences.
Lead cross-functional research threads to bridge prototype model development with production deployment.
Translate complex technical research into tangible business goals for stakeholders and product leaders.
Own and pursue a research agenda by identifying impactful problems and autonomously carrying out long-running projects.
Develop and deliver libraries, platform-level code, or solution-level code for existing products.
What we're looking for
Must have a PhD in a relevant field with 2 years of applied research experience or an M.S. in a relevant field with 4 years of experience.
Must have hands-on experience developing AI foundation models and solutions using open-source tools and cloud computing platforms.
Must have a deep understanding of AI methodology foundations and experience building large deep learning models for language, images, events, or graphs.
Must have expertise in at least one of the following: training optimization, self-supervised learning, robustness, explainability, or RLHF.
Must have an engineering mindset with a track record of delivering models at scale for training data and inference volumes.
Must have experience delivering libraries, platform-level code, or solution-level code to existing products.
Must have a track record of high-quality machine learning ideas, such as first-author publications or significant projects.
Must possess the ability to autonomously own a research agenda and choose impactful research problems.
Must have a PhD in Computer Science, Machine Learning, or related fields (preferred).
Must have an LLM PhD focus on NLP or a Master's with 5 years of industrial NLP research experience (preferred).
Must have multiple publications on pre-training large language models, SSL techniques, or model pre-training optimization (preferred).
Must have been a member of a team that trained a large language model from scratch with 10B+ parameters (preferred).
Must have publications in deep learning theory or at top-tier venues like ACL, Neurips, ICML, or ICLR (preferred).
Must have experience optimizing training for a 10B+ model (preferred).
Must have experience with compiler design or fine-tuning PhDs focused on guiding LLMs (preferred).
Must have a proven ability to design and lead small-scale research initiatives and guide junior researchers (preferred).
Pytorch
AWS
Huggingface
Lightning
LLM
NLP
Deep Learning
RLHF
Quantization
Model Sparsification
Training Parallelism
Supervised Finetuning
Instruction-Tuning
Transfer Learning
Pytorch
AWS
Huggingface
Lightning
LLM
NLP
Deep Learning
RLHF
Self-Supervised Learning
Quantization
Model Compression
Training Parallelism
Supervised Finetuning
Instruction-Tuning
Transfer Learning
ML Engineering