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 $219k

This role $234k

$180k most similar roles pay here $257k

This role pays more than 72% of similar roles. Most pay $192,050–$246,150 — the shaded band above. At the midpoint, this role pays about $234k versus about $219k 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

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Applied Researcher 4 (AI Foundations - LLM, Optimization and Finetuning) joins the AI Foundations team to develop trustworthy and reliable AI systems for banking applications. This role involves collaborating with cross-functional teams of data scientists and engineers to build AI foundation models through every phase of development, including design, training, evaluation, validation, and implementation. The researcher will perform high-impact applied research to translate complex technical findings into tangible business goals while preparing internal reports or conference submissions. The work requires expertise in large deep learning models involving language, images, events, or graphs, with specific focus on training optimization, self-supervised learning, robustness, explainability, and RLHF. Key technologies include Pytorch, AWS Ultraclusters, Huggingface, and Lightning. The role addresses the challenge of extracting insights from massive volumes of numeric and textual data to improve customer experiences.

Skills

LLM Pytorch AWS Huggingface Lightning Deep Learning NLP RLHF Self-Supervised Learning Quantization Model Sparsification Training Optimization Compiler Design Finetuning Transfer 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 experiences.
Develop large deep learning models using PyTorch, Hugging Face, and cloud computing platforms like AWS.
Optimize model training and inference through techniques like quantization, sparsification, and parallel training design.
Perform fine-tuning tasks including supervised fine-tuning, instruction tuning, and parameter tuning for LLMs.
Translate complex technical research into tangible business goals and internal technical reports or conference submissions.
Identify and solve large, undefined problems by researching and evaluating emerging AI technologies.
Own and execute a research agenda by selecting impactful problems and managing long-running projects independently.

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 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 demonstrate 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.
LLM PhD focus on NLP or Masters with 5 years of industrial NLP research experience (preferred).
Multiple publications on pre-training large language models, SSL techniques, or model optimization (preferred).
Membership in a team that has trained a large language model from scratch with over 10B parameters (preferred).
Publications in deep learning theory or at major conferences like Neurips, ICML, or ICLR (preferred).
Experience in training optimization, self-supervised learning, robustness, explainability, or RLHF (preferred).
Experience with model sparsification, quantization, training parallelism, or compiler design (preferred).
PhD focused on fine-tuning topics such as supervised finetuning or instruction-tuning (preferred).

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