Applied Researcher IV

Capital One Financial

Confirmed live yesterday High trust

Quick summary

Work type
On-site
Location
San Jose, CASan Francisco, CAMcLean, VACambridge, MANew York, NY
Salary
$218,700–$249,600 / yr
Posted
3 days ago
Freshness
Confirmed live yesterday
Nearby
99+ roles within 25 mi

Market check

Salary context

Competitive pay

How this pay compares to similar roles

Similar $224k
This role $234k
$174k most similar roles pay here $273k

This role pays more than 55% of similar roles. Most pay $193,935–$254,750 — the shaded band above. At the midpoint, this role pays about $234k versus about $224k 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 IV

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As an Applied Researcher 4 (AI Foundations, LLM Core and Agentic AI), you will join the AI Foundations team to develop trustworthy and reliable AI systems for banking services. You will collaborate with a cross-functional team of data scientists, software engineers, and product managers to build 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 to create next-generation customer experiences. To achieve these goals, you will utilize a technical stack featuring PyTorch, AWS Ultraclusters, Huggingface, and Lightning. The role requires expertise in large deep learning models, training optimization, self-supervised learning, and RLHF. You will translate complex research into tangible business goals while managing the full research lifecycle to solve problems related to automated customer interactions.

Skills

Pytorch AWS Huggingface Lightning LLM NLP Deep Learning RLHF Self-Supervised Learning Quantization Model Sparsification Training Optimization Compiler Design Machine Learning

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 Ultraclusters.
  • Translate complex technical research findings into tangible business goals for stakeholders and product managers.
  • Prepare internal technical reports or conference submissions to summarize novel methods and research findings.
  • Develop scalable production code, libraries, and platform-level solutions for high-volume inference and training.
  • Identify and solve large, undefined problems by researching and evaluating emerging AI technologies.

What we're looking for

  • Must have a PhD in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or a related field.
  • Alternatively, must have an M.S. in a relevant technical field plus 2 years of experience in Applied Research.
  • Must have hands-on experience developing AI foundation models and solutions using open-source tools and cloud computing platforms.
  • Must possess a deep understanding of the foundations of AI methodologies.
  • Must have experience building large deep learning models for language, images, events, or graphs.
  • Must have an engineering mindset with a track record of delivering models at scale for training and inference.
  • Experience in delivering libraries, platform-level code, or solution-level code to existing products.
  • Ability to own and pursue a research agenda, including selecting impactful problems and autonomously carrying out long-running projects.

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