Applied Researcher II

Capital One Financial

Confirmed live 2 days ago Low trust

Quick summary

Work type
On-site
Location
New York, NYSan Francisco, CAMcLean, VACambridge, MASan Jose, CA
Salary
$262,500–$299,600 / yr
Posted
141 days ago
Freshness
Confirmed live 2 days ago
Nearby
99+ roles within 25 mi

Market check

Salary context

Above market

How this pay compares to similar roles

Similar $179k
This role $281k
$102k most similar roles pay here $321k

This role pays more than 95% of similar roles. Most pay $133,500–$224,111 — the shaded band above. At the midpoint, this role pays about $281k versus about $179k 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 998 open roles on FindRole.

Listed pay typically runs $197,300–$225,100 across 992 roles with salary data.

Most-posted roles

View all roles at Capital One Financial

At a glance

TL;DR · Applied Researcher II

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As an Applied Researcher II on the AI Foundations team, you will collaborate with cross-functional teams of data scientists, software engineers, and product managers to deliver AI-powered products that transform how customers interact with their money. You will build AI foundation models through every phase of development, including design, training, evaluation, validation, and implementation. To uncover insights within massive volumes of numeric and textual data, you will utilize a technical stack featuring PyTorch, AWS Ultraclusters, Huggingface, Lightning, and VectorDBs. The role involves conducting high-impact research to integrate state-of-the-art developments into next-generation customer experiences. You will solve complex problems related to large deep learning models involving language, images, events, or graphs, while applying specialized knowledge in areas such as training optimization, self-supervised learning, robustness, explainability, and RLHF to create reliable systems for the banking sector.

Skills

Pytorch AWS Huggingface Lightning VectorDBs Machine Learning Deep Learning NLP LLM RLHF Self-Supervised Learning Quantization Model Compression Training Optimization Cloud Computing

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.
  • Use a broad technology stack including PyTorch, Hugging Face, and VectorDBs to analyze large volumes of data.
  • Develop scalable deep learning models for language, images, events, or graphs.
  • Translate complex technical research into tangible business goals for stakeholders and product managers.
  • Research and evaluate emerging technologies to identify opportunities for practical application.
  • Own and pursue a research agenda by selecting impactful problems and 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 is required in areas such as training optimization, self-supervised learning, robustness, explainability, or RLHF.
  • Must have hands-on experience developing AI foundation models using open-source tools and cloud computing platforms.
  • Experience delivering libraries, platform-level code, or solution-level code to existing products is required.
  • Candidates should have a track record of innovation in machine learning, such as first-author publications or significant projects.
  • Ability to independently own and pursue a research agenda including selecting problems and managing long-running projects.
  • Proficiency with open-source languages and the ability to translate complex technical work into tangible business goals.

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