Applied Researcher IV

Capital One Financial

Confirmed live yesterday High trust

Quick summary

Work type
On-site
Location
San Jose, CAMcLean, VACambridge, MANew York, NY
Salary
$218,700–$249,600 / yr
Posted
3 days ago
Freshness
Confirmed live yesterday
Nearby
99+ roles within 25 mi

Market check

Salary context

Competitive pay

How this pay compares to similar roles

Similar $224k
This role $234k
$164k most similar roles pay here $273k

This role pays more than 59% of similar roles. Most pay $193,935–$254,750 — the shaded band above. At the midpoint, this role pays about $234k 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.

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View all roles at Capital One Financial

At a glance

TL;DR · Applied Researcher IV

Role Posting Log in to save

As an Applied Researcher 4 (AI Foundations, LLM Customization, Finetuning, Reinforcement Learning), you will join the AI Foundations team to develop trustworthy and reliable AI systems for banking applications. You will collaborate with a cross-functional team of data scientists, software engineers, and product managers to build AI foundation models through every phase of development, including design, training, evaluation, and implementation. Your daily work involves leveraging Pytorch, AWS Ultraclusters, Huggingface, and Lightning to extract insights from large volumes of numeric and textual data. You will conduct high-impact research on state-of-the-art methods like RLHF, self-supervised learning, and training optimization to improve customer experiences. The role focuses on solving complex problems in the financial sector by translating advanced AI research into scalable production systems that automate interactions and provide real-time information for customers and businesses.

Skills

LLM Pytorch Huggingface Lightning AWS Reinforcement Learning RLHF Self-Supervised Learning Graph Neural Networks NLP Deep Learning Finetuning Instruction-Tuning pytorch-geometric DGL

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 experiences.
  • Develop large deep learning models for language, images, events, or graphs using PyTorch and cloud platforms.
  • Implement advanced techniques such as RLHF, self-supervised learning, training optimization, and model explainability.
  • Translate complex technical research into tangible business goals for stakeholders and product managers.
  • Prepare internal technical reports or conference submissions to summarize novel methods and findings.
  • Deliver scalable production code, libraries, and platform-level solutions for high-volume inference.
  • Manage an independent research agenda by identifying impactful problems and executing long-running projects.

What we're looking for

  • Must have a PhD in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields.
  • Alternatively, must have an M.S. in a relevant field plus 2 years of experience in Applied Research.
  • Experience building large deep learning models and expertise in 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.
  • Ability to own a research agenda, including selecting impactful problems and autonomously carrying out long-running projects.
  • Demonstrated ability to reproduce and extend peer-reviewed AI research using modern open-source frameworks (preferred).
  • Multiple publications on topics like pre-training large language models or deep learning theory (preferred).

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