Staff Applied Researcher

Capital One Financial

Confirmed live yesterday Trusted

Quick summary

Work type
On-site
Location
McLean, VASan Francisco, CACambridge, MASan Jose, CANew York, NY
Salary
$278,400–$317,700 / yr
Posted
15 days ago
Freshness
Confirmed live yesterday

Market check

Salary context

Above market

How this pay compares to similar roles

Similar $213k
This role $298k
$143k most similar roles pay here $336k

This role pays more than 97% of similar roles. Most pay $177,250–$248,418 — the shaded band above. At the midpoint, this role pays about $298k versus about $213k 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 · Staff Applied Researcher

Staff Applied Researcher joins the AI Foundations team to drive strategic direction and lead research initiatives in the realm of advanced machine learning. This individual contributor role involves collaborating with cross-functional teams of data scientists, engineers, and product managers to build AI foundation models through every phase of development, including design, training, evaluation, validation, and implementation. The successful candidate will conduct high-impact applied research to integrate state-of-the-art developments into next-generation customer experiences. To uncover insights within massive volumes of numeric and textual data, the role utilizes a technical stack including PyTorch, AWS Ultraclusters, Huggingface, Lightning, and VectorDBs. The work focuses on solving complex problems in large-scale deep learning models involving language, images, events, or graphs, specifically addressing challenges like training optimization, self-supervised learning, robustness, explainability, and RLHF to improve how customers interact with their money.

What you'll do

  • Build AI foundation models through all phases of development including design, training, evaluation, validation, and implementation.
  • Conduct high-impact applied research to integrate the latest AI developments into next-generation customer experiences.
  • Utilize a broad technology stack including PyTorch, AWS Ultraclusters, Huggingface, and VectorDBs to analyze large volumes of data.
  • Partner with cross-functional teams of scientists, engineers, and product managers to deliver AI-powered products.
  • Translate complex technical research into tangible business goals for stakeholders.
  • Represent the company in the research community by collaborating with prominent academic faculty members.
  • Guide and mentor a team of applied scientists and their managers on technical direction.
  • Own and pursue an independent research agenda by selecting impactful problems and leading long-running projects.

What we're looking for

  • A PhD in Engineering, Computer Science, AI, or Mathematics with 4 years of experience in Applied Research is required.
  • An M.S. in Engineering, Computer Science, AI, or Mathematics with 6 years of experience in Applied Research is required.
  • Experience building large deep learning models and a track record of delivering models at scale for training and inference.
  • Expertise in areas such as training optimization, self-supervised learning, robustness, explainability, or RLHF.
  • Proficiency with technologies including PyTorch, AWS Ultraclusters, Huggingface, Lightning, and VectorDBs.
  • Experience developing AI foundation models through all phases of development from design to implementation.
  • A track record of research accomplishments such as first author publications in major conferences like NeurIPS, ICML, or ICLR.
  • Demonstrated ability to guide the technical direction of large-scale model training teams and translate complex work into business goals.

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