Applied Researcher I

Capital One Financial

Confirmed live 2 days ago High trust

Quick summary

Work type: On-site
Location: New York, NYSan Francisco, CAMcLean, VACambridge, MASan Jose, CA
Salary: $218,700–$249,600 / yr
Posted: 9 days ago
Freshness: Confirmed live 2 days ago
Nearby: 99+ roles within 25 mi

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Salary context

Competitive pay

How this pay compares to similar roles

Similar $216k

This role $234k

$153k most similar roles pay here $284k

This role pays more than 60% of similar roles. Most pay $177,250–$254,750 — the shaded band above. At the midpoint, this role pays about $234k versus about $216k 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 I

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As an Applied Researcher I 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 all development phases, including design, training, evaluation, validation, and implementation. Your daily work involves uncovering insights within large volumes of numeric and textual data to improve customer experiences. To achieve these goals, you will utilize a technical stack featuring PyTorch, AWS Ultraclusters, Huggingface, Lightning, and VectorDBs. The role requires expertise in deep learning models for language, images, events, or graphs, alongside specialized knowledge in areas such as training optimization, self-supervised learning, robustness, explainability, and RLHF. You will translate complex research into tangible business goals while managing a dedicated research agenda to solve high-impact problems.

Skills

PyTorch AWS Huggingface Lightning VectorDBs Machine Learning Deep Learning NLP LLM RLHF Quantization Model Compression Training Optimization Cloud Computing 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.
Utilize a broad technology stack including PyTorch, AWS Ultraclusters, HuggingFace, and VectorDBs to analyze large volumes of data.
Develop scalable deep learning models for language, images, events, or graphs using open-source tools and cloud platforms.
Translate complex technical research into tangible business goals and solutions for cross-functional stakeholders.
Identify and solve large, undefined problems by researching and evaluating emerging technologies and methodologies.
Own and pursue a research agenda by selecting impactful problems and autonomously managing long-running projects.

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 and large deep learning models.
Must have expertise in at least one area: training optimization, self-supervised learning, robustness, explainability, or RLHF.
Must demonstrate an engineering mindset with a track record of delivering models at scale for both training data and inference volumes.
Must have experience delivering libraries, platform-level code, or solution-level code to existing products.
Must have a track record of high-quality machine learning ideas, such as first-author publications or significant research projects.

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