Applied Researcher I – AI Foundations, VLM

Jobtailor; California (MO); On-site; USD 180,000 - 240,000; Full time

Job summary

Jobtailor is seeking a research scientist to advance AI foundation models, leading end-to-end development from design to deployment. You will collaborate with data scientists, software engineers, and product managers to translate cutting-edge AI research into customer-focused solutions.

Ideal candidates hold a PhD or MS with strong industrial NLP or ML research experience, with a track record of publications or large-scale model work and comfort with cloud computing platforms.

Qualifications

• PhD in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields; degree may be obtained on or before the scheduled start date.
• MS in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields plus 2 years of experience in Applied Research.
• Deep understanding of AI methodology foundations.
• Experience building large deep learning models for language, images, events, or graphs.
• Expertise in one or more of: training optimization, self-supervised learning, robustness, explainability, or RLHF.
• Track record delivering models at scale in training data and inference volumes.
• Experience delivering libraries, platform-level code, or solution-level code to existing products.
• Track record of high-quality machine-learning ideas, such as first-author publications or projects.
• Ability to own and pursue a research agenda and autonomously carry out long-running projects.
• Hands-on experience developing AI foundation models and solutions using open-source tools and cloud computing platforms.
• Preferred: PhD in a related technical field.
• Preferred: NLP focus or Master's with 5 years of industrial NLP research experience.
• Preferred: publications related to pre-training large language models, deep learning theory, or major ML conferences.
• Preferred: experience training a large language model from scratch with 10B+ parameters and 500B+ tokens.
• Preferred: experience optimizing training for a 10B+ model.
• Preferred: deep-learning algorithmic or optimizer design knowledge.
• Preferred: compiler design experience.
• Preferred: knowledge of transfer learning, model adaptation, and model guidance.
• Preferred: experience deploying a fine-tuned large language model.
• Capital One will consider sponsoring a new qualified applicant for employment authorization.

Responsibilities

• Partner with data scientists, software engineers, machine learning engineers, and product managers to deliver AI-powered products.
• Use PyTorch, AWS Ultraclusters, Hugging Face, Lightning, VectorDBs, and other technologies to uncover insights from large numeric and textual datasets.
• Build AI foundation models through design, training, evaluation, validation, and implementation.
• Conduct applied research and advance emerging AI developments into customer experiences.
• Translate complex technical work into tangible business goals.
• Research and evaluate emerging technologies and state-of-the-art AI methods.
• Define and solve ambiguous problems and improve the status quo with stakeholders.
• Contribute to talent development.

Skills

Deep Learning

NLP

AI Methodology Foundations

Training Optimization

Self-Supervised Learning

Robustness

Explainability

RLHF

Large Language Model Training

Education

PhD in Electrical Engineering/Computer Engineering/CS/AI/Mathematics

MS in EE/CE/CS/AI/Math + 2 years Applied Research

Tools

PyTorch

AWS Ultraclusters

Hugging Face

Lightning

VectorDBs

Open-Source Tools

Cloud Computing Platforms

Compiler Design

Job description

• Partner with data scientists, software engineers, machine learning engineers, and product managers to deliver AI-powered products
• Use PyTorch, AWS Ultraclusters, Hugging Face, Lightning, VectorDBs, and other technologies to uncover insights from large numeric and textual datasets
• Build AI foundation models through design, training, evaluation, validation, and implementation
• Conduct applied research and advance emerging AI developments into customer experiences
• Translate complex technical work into tangible business goals
• Research and evaluate emerging technologies and state-of-the-art AI methods
• Define and solve ambiguous problems and improve the status quo with stakeholders
• Contribute to talent development

Requirements

• Currently has, or is in the process of obtaining, a PhD in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields; required degree may be obtained on or before the scheduled start date
• Alternatively, an M.S. in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields plus 2 years of experience in Applied Research
• Deep understanding of AI methodology foundations
• Experience building large deep learning models for language, images, events, or graphs
• Expertise in one or more of: training optimization, self-supervised learning, robustness, explainability, or RLHF
• Track record delivering models at scale in training data and inference volumes
• Experience delivering libraries, platform-level code, or solution-level code to existing products
• Track record of high-quality machine-learning ideas, such as first-author publications or projects
• Ability to own and pursue a research agenda and autonomously carry out long-running projects
• Hands-on experience developing AI foundation models and solutions using open-source tools and cloud computing platforms
• Preferred: PhD in a related technical field
• Preferred: NLP focus or Master's degree with 5 years of industrial NLP research experience
• Preferred: publications related to pre-training large language models, deep learning theory, or major ML conferences
• Preferred: experience training a large language model from scratch with 10B+ parameters and 500B+ tokens
• Preferred: experience optimizing training for a 10B+ model
• Preferred: deep-learning algorithmic or optimizer design knowledge
• Preferred: compiler design experience
• Preferred: knowledge of transfer learning, model adaptation, and model guidance
• Preferred: experience deploying a fine-tuned large language model
• Capital One will consider sponsoring a new qualified applicant for employment authorization

Core Competencies

Demonstrates expertise in building and optimizing AI foundation models, with a strong focus on deep learning methodologies and large-scale model deployment. Proven ability to translate complex AI concepts into actionable business strategies while collaborating with cross-functional teams.

Highest-signal resume keywords

• PhD In Electrical Engineering
• Deep Learning Model Development
• AI Methodology Foundations
• Experience With PyTorch
• NLP Research Experience

Hard Skills

• Training Optimization
• Self-Supervised Learning
• Robustness
• Explainability
• Reinforcement Learning From Human Feedback
• Large Language Model Training
• Model Adaptation
• Model Guidance
• Compiler Design
• Deep Learning Theory

Soft Skills

• Problem Solving
• Collaboration
• Talent Development

Industry Keywords

• Applied Research
• Machine Learning
• Natural Language Processing
• AI-Powered Products
• Emerging Technologies

Tools & Technologies

• AWS Ultraclusters
• Hugging Face
• Lightning
• VectorDBs
• Open-Source Tools
• Cloud Computing Platforms