Applied Researcher V

Capital One Financial

Confirmed live yesterday High trust

Quick summary

Work type
On-site
Location
McLean, VACambridge, MASan Jose, CANew York, NY
Salary
$262,500–$299,600 / yr
Posted
3 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 $221k
This role $281k
$149k most similar roles pay here $316k

This role pays more than 88% of similar roles. Most pay $186,975–$254,750 — the shaded band above. At the midpoint, this role pays about $281k versus about $221k 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.

Most-posted roles

View all roles at Capital One Financial

At a glance

TL;DR · Applied Researcher V

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As an Applied Researcher 5 on the AI Foundations team, you will collaborate with cross-functional teams of data scientists, software engineers, and product managers to develop AI-powered products for banking services. 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 featuring PyTorch, AWS Ultraclusters, Huggingface, and Lightning. The role focuses on the core challenge of applying state-of-the-art machine learning to create reliable, automated customer experiences. You will specifically work on large deep learning models involving language, images, events, or graphs while addressing problems like training optimization, self-supervised learning, and model robustness.

Skills

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

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 state-of-the-art AI developments into next-generation customer experiences.
  • Analyze large volumes of numeric and textual data using tools like PyTorch, Hugging Face, and AWS Ultraclusters.
  • Lead cross-functional research threads to bridge the gap between prototype model development and production deployment.
  • Translate complex technical research into tangible business goals for product managers and stakeholders.
  • Research and evaluate emerging technologies to identify opportunities for application within the company's AI infrastructure.
  • Develop scalable models and high-quality code for large-scale training data and inference volumes.

What we're looking for

  • Must have a PhD in a relevant field with 2 years of experience or an MS with 4 years of experience in Applied Research.
  • Experience building large deep learning models for language, images, events, or graphs is required.
  • Expertise in training optimization, self-supervised learning, robustness, explainability, or RLHF is required.
  • Must have a track record of delivering models at scale regarding both training data and inference volumes.
  • Experience delivering libraries, platform-level code, or solution-level code to existing products is required.
  • Proven ability to own a research agenda and independently carry out long-running projects is required.
  • A track record of high-quality ideas in machine learning, such as first-author publications, is required.
  • Experience with LLM pre-training, optimization, or fine-tuning is preferred; multiple publications in top-tier venues are preferred.

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