Applied Researcher IV

Capital One Financial

Confirmed live yesterday High trust

Quick summary

Work type
On-site
Location
New York, NYMcLean, VACambridge, MASan Jose, CA
Salary
$218,700–$249,600 / yr
Employment
Full-time
Posted
7 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 $203k
This role $234k
$185k most similar roles pay here $257k

This role pays more than 89% of similar roles. Most pay $192,050–$213,250 — the shaded band above. At the midpoint, this role pays about $234k versus about $203k 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 936 open roles on FindRole.

Listed pay typically runs $197,300–$225,100 across 933 roles with salary data.

Most-posted roles

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 on the AI Foundations team, you will join a cross-functional group of data scientists, software engineers, and product managers to develop AI-powered products for banking experiences. You will be responsible for building AI foundation models through every phase of development, including design, training, evaluation, validation, and implementation. Your daily work involves uncovering insights within massive volumes of numeric and textual data while translating complex research into tangible business goals. To achieve these objectives, you will utilize a technical stack including PyTorch, AWS Ultraclusters, Huggingface, and Lightning. The role focuses on the practical application of state-of-the-art AI to solve problems involving large deep learning models across language, images, events, or graphs, specifically addressing challenges in training optimization, self-supervised learning, robustness, explainability, and reinforcement learning from human feedback.

Skills

PyTorch AWS Huggingface Lightning LLM NLP Deep Learning RLHF Self-Supervised Learning Quantization Model Compression Training Optimization Compiler Design Instruction-Tuning

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.
  • Analyze large volumes of numeric and textual data using tools like PyTorch, Hugging Face, and AWS.
  • Translate complex technical research into tangible business goals for stakeholders and product managers.
  • Prepare internal technical reports or conference submissions summarizing novel methods and findings.
  • Develop scalable models and production-level code to handle high training and inference volumes.
  • Identify and solve large, undefined problems by researching and evaluating emerging technologies.

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 for language, images, events, or graphs is required.
  • Must possess a track record of delivering models at scale regarding training data and inference volumes.
  • Required expertise in one or more areas: training optimization, self-supervised learning, robustness, explainability, or RLHF.
  • Experience developing AI foundation models using open-source tools and cloud computing platforms is required.
  • Must have a track record of high-quality machine learning ideas, such as first author publications or significant projects (preferred).
  • Specific advanced qualifications include experience in model compression, compiler design, or fine-tuning large language models (preferred).

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