Applied Researcher 5, AI Foundations, LLM Core and Agentic AI

Capital One Financial

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Quick summary

Work type
On-site
Location
New York, NYMcLean, VACambridge, MASan Jose, CA
Salary
$262,500–$299,600 / yr
Employment
Full-time
Posted
49 days ago
Freshness
Confirmed live today
Nearby
99+ roles within 25 mi

Market check

Salary context

Above market

How this pay compares to similar roles

Similar $233k
This role $281k
$185k $312k
below market most similar roles pay here above market

This role pays more than 87% 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.

Most-posted roles

View all roles at Capital One Financial

At a glance

TL;DR · Applied Researcher 5, AI Foundations, LLM Core and Agentic AI

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The Applied Researcher 5 (AI Foundations, LLM Core and Agentic AI) joins the AI Foundations team to develop trustworthy and reliable AI systems. You will partner with a cross-functional team of data scientists, software engineers, and product managers to build AI foundation models through all phases of development, including design, training, evaluation, validation, and implementation. Your daily work involves leveraging a broad stack of technologies including Pytorch, AWS Ultraclusters, Huggingface, and Lightning to extract insights from massive volumes of numeric and textual data. You will lead research threads that bridge prototype model development and production deployment. The role focuses on applying state-of-the-art AI methodologies, such as self-supervised learning, RLHF, and training optimization, to solve complex business problems and create next-generation customer experiences in the banking sector.

Skills

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

What you'll do

  • Build AI foundation models through all phases of development including design, training, evaluation, and implementation.
  • Leverage technologies like Pytorch, AWS Ultraclusters, and Huggingface to extract insights from large numeric and textual datasets.
  • Lead cross-functional research threads to bridge prototype model development with production deployment.
  • Translate complex research findings into tangible business goals and customer experiences.
  • Conduct high-impact applied research to integrate the latest AI developments into next-generation products.
  • 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.

What we're looking for

  • Must have a PhD in a relevant field with 2 years of applied research experience or an MS with 4 years of experience.
  • Must have hands-on experience developing AI foundation models and solutions using open-source tools and cloud platforms.
  • Must have a deep understanding of AI methodology foundations and experience building large deep learning models.
  • Must have expertise in 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 and inference.
  • 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 projects.
  • Must possess the ability to autonomously own and pursue a research agenda and choose impactful research problems.

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