Applied Researcher V

Capital One Financial

Confirmed live yesterday High trust

Quick summary

Work type
On-site
Location
San Jose, CASan Francisco, CAMcLean, VACambridge, MANew York, NY
Salary
$262,500–$299,600 / yr
Posted
3 days ago
Freshness
Confirmed live yesterday
Nearby
99+ roles within 25 mi

Market check

Salary context

Above market

How this pay compares to similar roles

Similar $224k
This role $281k
$171k most similar roles pay here $313k

This role pays more than 88% of similar roles. Most pay $193,001–$254,750 — the shaded band above. At the midpoint, this role pays about $281k versus about $224k 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.

Most-posted roles

View all roles at Capital One Financial

At a glance

TL;DR · Applied Researcher V

Role Posting Log in to save

Applied Researcher 5 (AI Foundations, LLM Core and Agentic AI) joins the AI Foundations team to develop advanced machine learning systems for banking applications. This role involves collaborating with cross-functional teams of data scientists and engineers to build AI foundation models through every phase of development, including design, training, evaluation, and implementation. The researcher will translate complex scientific insights into production-ready features while managing research threads that bridge prototype development and deployment. Key technologies include PyTorch, AWS Ultraclusters, Huggingface, and Lightning. Candidates must possess expertise in large deep learning models involving language, images, events, or graphs, with specific knowledge in areas like RLHF, training optimization, and self-supervised learning. The role focuses on solving complex problems related to high-volume numeric and textual data to create automated customer experiences within the financial services domain.

Skills

Pytorch AWS Huggingface Lightning LLM NLP Deep Learning RLHF Self-Supervised Learning Quantization Model Sparsification Training Parallelism Compiler Design Finetuning

What you'll do

  • 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.
  • Develop production-ready AI solutions using a stack including PyTorch, Hugging Face, and AWS infrastructure.
  • Translate complex technical research into tangible business goals for cross-functional stakeholders.
  • Lead research threads that bridge the gap between prototype model development and large-scale production deployment.
  • Identify and solve complex, undefined problems by researching and evaluating emerging technologies.
  • Manage a research agenda by selecting impactful problems and autonomously executing long-running projects.

What we're looking for

  • Must have a PhD in a relevant field with 2 years of experience or an MS degree with 4 years of experience in Applied Research.
  • Experience building large deep learning models for language, images, events, or graphs is required.
  • Expertise in at least one area including training optimization, self-supervised learning, robustness, explainability, or RLHF is required.
  • Must have a track record of delivering models at scale regarding both training data and inference volumes.
  • Experience delivering libraries, platform-level code, or solution-level code to existing products is required.
  • Proven ability to own a research agenda and independently carry out long-running projects is required.
  • A track record of high-quality ideas in machine learning, such as first-author publications, is required.
  • Experience with LLM pre-training, optimization, or fine-tuning techniques is preferred.

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