Applied Researcher 4 – Red Teaming, Multimodal

Capital One

. Partner with data scientists, software engineers, machine learning engineers, and product managers to deliver AI-powered products .

Posted 11/4/2026full-timeUnited StatesJuniorMid-Level💰 $218,700 - $272,300 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in AI methodologies and deep learning model development, with hands-on experience in building and deploying AI foundation models using tools like PyTorch and AWS. Proven ability to translate complex research into actionable business outcomes and deliver high-quality machine learning solutions at scale.

Highest-signal resume keywords

PhD In Relevant FieldDeep Learning Model DevelopmentAI Methodologies ExpertiseExperience With PyTorchTrack Record Of High-Quality Machine Learning Ideas

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Deep LearningAI Foundation ModelsTraining OptimizationSelf-Supervised LearningRobustnessExplainabilityRed TeamingModel SparsificationQuantizationTransfer Learning

Soft Skills

CollaborationResearch Agenda OwnershipAutonomous Project Management

Tools & Technologies

PyTorchAWS UltraclustersHugging FaceLightningOpen-Source ToolsCloud Computing Platforms

Industry Keywords

Applied ResearchNatural Language ProcessingMachine LearningModel CompressionControlled ExperimentsPublications In AI Research

Tech Stack

Tools & technologies

AWSCloudPyTorch

About the role

Key responsibilities & impact

Partner with data scientists, software engineers, machine learning engineers, and product managers to deliver AI-powered products
Leverage PyTorch, AWS Ultraclusters, Hugging Face, Lightning, and related technologies to analyze large volumes of numeric and textual data
Build AI foundation models through design, training, evaluation, validation, and implementation
Conduct applied research to advance AI developments into customer experiences
Translate complex research into tangible business goals
Collaborate with senior researchers on internal technical reports or conference submissions describing novel methods or findings

Requirements

What you’ll need

Currently has, or is in the process of obtaining, a PhD in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields, with the degree obtained by the scheduled start date; or an M.S. in those fields plus 2 years of experience in Applied Research
Deep understanding of AI methodologies
Experience building large deep learning models involving language, images, events, or graphs
Expertise in one or more of training optimization, self-supervised learning, robustness, explainability, or RLHF
Experience with red teaming and deploying safety guardrails for multimodal systems, including VLMs, MLLMs, and MM-RAG architectures
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 or improvements, demonstrated by accomplishments 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 relevant field
Preferred: LLM PhD focus on NLP or master's degree with 5 years of industrial NLP research experience
Preferred: Publications related to pre-training large language models, deep learning theory, ACL/NAACL/EMNLP/NeurIPS/ICML/ICLR, or related topics
Preferred: Experience training large language models from scratch, including 10B+ parameters and 500B+ tokens
Preferred: Experience with model sparsification, quantization, training parallelism/partitioning, gradient checkpointing, or model compression
Preferred: Experience optimizing training for a 10B+ model
Preferred: Deep knowledge of deep learning algorithms and/or optimizer design
Preferred: Experience with compiler design
Preferred: Experience with supervised finetuning, instruction tuning, dialogue finetuning, or parameter tuning
Preferred: Knowledge of transfer learning, model adaptation, and model guidance
Preferred: Experience deploying a fine-tuned large language model
Preferred: Ability to reproduce and extend peer-reviewed AI research using modern open-source frameworks
Preferred: Experience designing controlled experiments and documenting reproducibility results
Capital One will consider sponsoring a new qualified applicant for employment authorization

Benefits

Comp & perks

Performance based incentive compensation, which may include cash bonuses and/or long-term incentives (LTI)
Comprehensive, competitive, and inclusive health, financial, and other benefits supporting total well-being
Reasonable accommodations for applicants who require them