Applied Researcher II (AI Foundations, LLM Core and Agentic AI)

Capital One Financial

Quick summary

Work type: On-site
Location: New York, NY · San Francisco, CA · McLean, VA · Cambridge, MA · San Jose, CA
Salary: $262,500–$299,600 / yr
Posted: 50 days ago

Market check

Salary context

Above market

How this pay compares to similar roles

Similar $210k

This role $281k

$155k most similar roles pay here $315k

This role pays more than 89% of similar roles. Most pay $174,800–$246,150 — the shaded band above. At the midpoint, this role pays about $281k versus about $210k 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 498 open roles on FindRole.

Listed pay typically runs $197,300–$225,100 across 495 roles with salary data.

Most-posted roles

View all roles at Capital One Financial

At a glance

TL;DR · Applied Researcher II (AI Foundations, LLM Core and Agentic AI)

Apply Now Log in to save

As an Applied Researcher II on the AI Foundations team focusing on LLM core and agentic AI, you will collaborate with a cross-functional group including data scientists, software engineers, machine learning engineers, and product managers to develop innovative AI products that transform customer interactions. Your daily tasks involve analyzing large datasets, designing and implementing advanced NLP models, optimizing training processes for massive language models, and deploying fine-tuned LLMs in real-world applications. Ideal candidates possess a PhD or equivalent experience in fields like Computer Science, Machine Learning, or Applied Mathematics, with expertise in pre-training large language models, deep learning optimization techniques, and transfer learning principles. Proficiency in Python, TensorFlow, PyTorch, and familiarity with cloud platforms such as AWS or Azure is essential. This role requires deep knowledge of model sparsification, quantization, and parallel training strategies to tackle the computational challenges of scaling AI solutions at Capital One.

Skills

Python TensorFlow PyTorch Kubernetes Docker CI/CD PostgreSQL AWS Git CUDA LLM NLP ACL NAACL EMNLP Neurips ICML ICLR Model Optimization Gradient Checkpointing

What you'll do

Analyze and create AI-powered products for customer interaction.
Train large language models from scratch with over 10B parameters.
Optimize training processes for very large deep learning models.
Design and implement techniques for model sparsification and quantization.
Deploy fine-tuned large language models in production environments.
Conduct research on transfer learning principles and model adaptation.

What we're looking for

PhD in Computer Science, Machine Learning, or related field.
Experience in NLP research with multiple publications on LLM pre-training.
Member of a team that trained a large language model from scratch.
Publications and experience in deep learning optimization techniques.
Expertise in deploying fine-tuned large language models.
Knowledge of transfer learning principles and model guidance strategies.
Deep understanding of deep learning algorithms, optimizers, and compiler design.

Save