below marketmost similar roles pay hereabove market
This listing doesn't post a salary. Most similar roles pay
$128,625–$220,212.
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
2107 open roles
on FindRole.
Listed pay typically runs
$197,300–$225,100
across 1107 roles with salary data.
The Applied Researcher II joins the AI Foundations team to develop trustworthy and reliable AI systems for banking. This role involves partnering with a cross-functional team of data scientists, software engineers, and product managers to deliver AI-powered products. You will build AI foundation models through all phases of development, including design, training, evaluation, validation, and implementation. Key responsibilities include conducting high-impact applied research to push the latest AI developments into next-generation customer experiences. You will utilize a technology stack including Pytorch, AWS Ultraclusters, Huggingface, Lightning, and VectorDBs to analyze large volumes of numeric and textual data. The work focuses on solving technical problems related to training optimization, self-supervised learning, robustness, explainability, and RLHF for large deep learning models.
Build AI foundation models through all phases of development including design, training, evaluation, and implementation.
Leverage technologies like Pytorch, AWS Ultraclusters, and VectorDBs to extract insights from large numeric and textual datasets.
Conduct high-impact applied research to integrate the latest AI developments into next-generation customer experiences.
Deliver AI-powered products by partnering with cross-functional teams of data scientists, software engineers, and product managers.
Translate complex technical research into tangible business goals for stakeholders.
Own and pursue a research agenda by identifying impactful problems and autonomously carrying out long-running projects.
Deliver libraries, platform-level code, or solution-level code to existing products at scale.
What we're looking for
Must have a PhD in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or a related field with 2 years of applied research experience.
Must have an M.S. in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or a related field with 4 years of applied research experience.
Must have hands-on experience developing AI foundation models and solutions using open-source tools and cloud computing platforms.
Must have experience building large deep learning models for language, images, events, or graphs.
Must have expertise in training optimization, self-supervised learning, robustness, explainability, or RLHF.
Must have a track record of delivering models at scale in terms of 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 new ideas or improvements in machine learning, such as first-author publications or projects.
Pytorch
AWS
Huggingface
Lightning
VectorDBs
LLM
NLP
Deep Learning
RLHF
Quantization
Model Sparsification
Training Parallelism
Graph Neural Networks
Sequential Models
Multivariate Time Series
Supervised Finetuning
Instruction-Tuning
Transfer Learning
Tokenization
Pytorch
AWS
Huggingface
Lightning
VectorDBs
LLM
NLP
Deep Learning
RLHF
Quantization
Model Sparsification
Training Parallelism
Supervised Finetuning
Instruction-Tuning
Transfer Learning
Pytorch
AWS
Huggingface
Lightning
VectorDBs
LLM
NLP
Deep Learning
RLHF
Quantization
Model Sparsification
Training Parallelism
Supervised Finetuning
Instruction-Tuning
Transfer Learning
Pytorch
AWS
Huggingface
Lightning
VectorDBs
LLM
NLP
Deep Learning
RLHF
Quantization
Model Sparsification
Training Parallelism
Graph Neural Networks
Sequential Models
Multivariate Time Series
Supervised Finetuning
Instruction-Tuning
Transfer Learning
Tokenization