Applied Researcher II

Capital One Financial

Confirmed live yesterday Trusted

Quick summary

Work type: On-site
Location: San Jose, CASan Francisco, CAMcLean, VACambridge, MANew York, NY
Salary: $262,500–$299,600 / yr
Posted: 50 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 $179k

This role $281k

$102k most similar roles pay here $321k

This role pays more than 95% of similar roles. Most pay $133,500–$224,111 — the shaded band above. At the midpoint, this role pays about $281k versus about $179k 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 998 open roles on FindRole.

Listed pay typically runs $197,300–$225,100 across 992 roles with salary data.

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View all roles at Capital One Financial

At a glance

TL;DR · Applied Researcher II

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As an Applied Researcher II on the AI Foundations team, you will collaborate with cross-functional teams of data scientists, software engineers, and product managers to deliver AI-powered products that transform how customers interact with their money. You will be responsible for building AI foundation models through every phase of development, including design, training, evaluation, validation, and implementation. To uncover insights within large volumes of numeric and textual data, you will utilize a technical stack featuring PyTorch, AWS Ultraclusters, Huggingface, Lightning, and VectorDBs. The role involves conducting high-impact applied research to integrate state-of-the-art developments into the next generation of customer experiences. You will solve complex problems by developing large deep learning models for language, images, events, or graphs while leveraging expertise in training optimization, self-supervised learning, robustness, explainability, and RLHF.

Skills

PyTorch AWS Huggingface Lightning VectorDBs LLM NLP Graph Neural Networks RLHF Quantization Model Sparsification Training Parallelism Supervised Finetuning 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.
Utilize a broad technology stack including PyTorch, AWS Ultraclusters, Hugging Face, and VectorDBs to analyze large datasets.
Translate complex technical research into tangible business goals for stakeholders and product managers.
Identify and solve large, undefined problems by researching and evaluating emerging technologies.
Develop scalable machine learning models for high volumes of training data and inference.
Deliver production-ready code including libraries, platform-level tools, or solution-level code to existing products.
Own and pursue a research agenda by selecting impactful problems and executing long-running projects autonomously.

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 hands-on experience developing AI foundation models using open-source tools and cloud computing platforms.
Experience delivering libraries, platform-level code, or solution-level code to existing products is required.
Ability to own a research agenda and independently carry out long-running projects is required.
A track record of innovation in machine learning, such as first-author publications or significant projects, is preferred.
Experience with large-scale training (10B+ parameters) or specific domains like NLP, graph models, or inference optimization is preferred.

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