Staff AI Engineer

Capital One

. Partner with engineers, research scientists, technical program managers, and product managers to deliver AI-powered products .

Posted 9/22/2026full-timeRemote • United StatesLead💰 $244,700 - $279,200 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing, developing, and deploying AI systems with a focus on scalability, cost, and performance. Proficient in leading engineering teams and influencing cross-functional stakeholders while championing responsible AI principles.

Highest-signal resume keywords

AI System DesignPython ProgrammingCloud DeploymentAI Architecture StandardsTeam Leadership

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

AI AlgorithmsMachine Learning TechnologiesFoundation Model TrainingLarge Language Model InferenceModel Optimization TechniquesAgentic AI SystemsData Pipeline GovernanceModel EvaluationScalable AI SolutionsMulti-Cloud AI Strategies

Soft Skills

MentorshipCross-Functional InfluenceBest Practices Dissemination

Tools & Technologies

AWSGoogle CloudAzureHugging FacePyTorchVectorDBsAWS Ultraclusters

Certifications & Qualifications

Bachelor's Degree in Computer ScienceMaster's Degree in AI or Related Fields

Industry Keywords

Responsible AI PrinciplesTransparencyReproducibilityFairness-by-DesignObservability

Tech Stack

Tools & technologies

AWSAzureCloudJavaOpen SourcePythonPyTorchScalaC++Go

About the role

Key responsibilities & impact

Partner with engineers, research scientists, technical program managers, and product managers to deliver AI-powered products
Design, develop, test, deploy, and support AI software components including foundation model training, large language model inference, agents and multi-agent workflows, similarity search, guardrails, model evaluation, experimentation, governance, and observability
Leverage Open Source and SaaS AI technologies such as AWS Ultraclusters, Hugging Face, VectorDBs, and PyTorch
Invent and introduce foundation model optimization techniques to improve scalability, cost, latency, and throughput of large-scale production AI systems
Contribute to the technical vision and long-term roadmap of foundational AI systems
Set technical direction for enterprise-wide AI architecture, tooling, observability, and deployment standards
Own the design and integration of model routing, caching, and orchestration systems for hybrid and multi-model workloads
Champion responsible AI principles, including transparency, reproducibility, and fairness-by-design
Drive internal education, mentorship, and dissemination of best practices through architecture councils and AI guilds

Requirements

What you’ll need

Bachelor's degree in Computer Science, AI, Electrical Engineering, Computer Engineering, or related fields plus at least 8 years of experience developing AI and ML algorithms or technologies; or a Master's degree in those fields plus at least 6 years of experience
At least 8 years of experience programming with Python, Go, Scala, CUDA, or Java
Experience designing AI systems with tradeoff decisions around cost, latency, throughput, and accuracy
Experience deploying scalable and responsible AI solutions on cloud platforms such as AWS, Google Cloud, Azure, or equivalent private cloud
Experience architecting, designing, developing, integrating, delivering, and supporting complex AI systems
Experience leading and mentoring multiple engineering teams and influencing cross-functional stakeholders up to the VP level
Experience developing AI and ML algorithms or technologies using Python, C++, C#, Java, CUDA, or Golang
Experience developing and applying state-of-the-art techniques for optimizing training and inference software
Experience building agentic AI systems and agentic workflows
Experience defining and operationalizing enterprise AI architecture standards, including data pipeline governance, observability, and evaluation frameworks
Experience leading federated or multi-cloud AI strategies
Experience influencing research-to-production promotion processes
Experience defining north-star metrics for AI systems
Experience right-sizing models, instance counts, and hardware types
Capital One will consider sponsoring a new qualified applicant for employment authorization

Benefits

Comp & perks

Performance based incentive compensation, which may include cash bonus(es) 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