Applied Researcher 4, AI Foundations: Recommendation Systems

Capital One Financial

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Quick summary

Work type: On-site
Location: McLean, VACambridge, MASan Jose, CANew York, NY
Salary: $218,700–$249,600 / yr
Employment: Full-time
Posted: 11 days ago
Freshness: Confirmed live today
Nearby: 99+ roles within 25 mi

Market check

Salary context

Above market

How this pay compares to similar roles

Similar $213k

This role $234k

$185k $257k

below market most similar roles pay here above market

This role pays more than 79% of similar roles. Most pay $192,050–$234,150 — the blue band above. At the midpoint, this role pays about $234k versus about $213k 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 2107 open roles on FindRole.

Listed pay typically runs $197,300–$225,100 across 1107 roles with salary data.

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At a glance

TL;DR · Applied Researcher 4, AI Foundations: Recommendation Systems

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The Applied Researcher 4 (AI Foundations: Recommendation Systems) joins the AI Foundations team to develop trustworthy and reliable AI systems. This role involves partnering with cross-functional teams of data scientists, software engineers, and product managers to build AI foundation models through all phases of development, including design, training, evaluation, validation, and implementation. You will leverage a broad technology stack including Pytorch, AWS Ultraclusters, Huggingface, and Lightning to extract insights from large volumes of numeric and textual data. Key responsibilities include conducting high-impact applied research, translating complex technical work into business goals, and preparing internal tech reports or conference submissions. The work focuses on pushing state-of-the-art AI developments into production to improve customer experiences and solve complex problems involving large deep learning models.

Skills

Pytorch AWS Huggingface Lightning Deep Learning NLP LLM RLHF Self-Supervised Learning Quantization Model Sparsification Training Parallelism Compiler Design Supervised Finetuning Instruction-Tuning Transfer Learning

What you'll do

Build AI foundation models through all phases of development including design, training, evaluation, and implementation.
Leverage technologies like Pytorch, AWS Ultraclusters, and Huggingface to extract insights from large numeric and textual datasets.
Conduct high-impact applied research to integrate the latest AI developments into next-generation customer experiences.
Deliver AI-powered products by partnering with cross-functional teams of data scientists, software engineers, and product managers.
Translate complex technical work into tangible business goals for stakeholders.
Prepare internal tech reports or conference submissions summarizing novel methods and research findings.
Own and pursue a research agenda by identifying impactful problems and autonomously carrying out long-running projects.
Develop and deliver libraries, platform-level code, or solution-level code to existing products.

What we're looking for

Currently hold or are obtaining a PhD in a relevant field, or an M.S. plus 2 years of experience in Applied Research.
Have hands-on experience developing AI foundation models and solutions using open-source tools and cloud computing platforms.
Possess a deep understanding of AI methodology foundations and experience building large deep learning models for language, images, events, or graphs.
Demonstrate expertise in training optimization, self-supervised learning, robustness, explainability, or RLHF.
Maintain an engineering mindset with a track record of delivering models at scale for training data and inference volumes.
Experience delivering libraries, platform-level code, or solution-level code to existing products.
Possess a track record of high-quality machine learning ideas, such as first-author publications or significant projects.
Ability to independently own a research agenda and autonomously carry out long-running projects.

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