Staff AI Engineer

Capital One

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

Posted 9/29/2026full-timeRemote • United StatesLead💰 $244,700 - $335,100 per yearWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in designing and deploying scalable AI systems, with a strong focus on optimization techniques for performance and cost. Proven ability to lead engineering teams and influence cross-functional stakeholders while championing responsible AI principles.

Highest-signal resume keywords

AI System DesignPython ProgrammingCloud DeploymentAI Architecture StandardsTeam Leadership

ATS Keywords

Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills

AI AlgorithmsMachine LearningLLM InferenceSimilarity SearchModel OptimizationFoundation Model TrainingHybrid WorkflowsData Pipeline GovernancePerformance MetricsModel Routing

Soft Skills

Excellent CommunicationMentorshipCross-Functional Influence

Tools & Technologies

AWSGoogle CloudAzureHugging FaceVectorDBsPyTorchCUDA

Industry Keywords

Responsible AIObservabilityGovernanceEnterprise AI ArchitectureFederated AI Strategies

Tech Stack

Tools & technologies

AWSAzureCloudJavaPythonPyTorchScalaGo

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, LLM 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 state-of-the-art foundation model optimization techniques to improve scalability, cost, latency, and throughput
Contribute to the technical vision and long-term roadmap of foundational AI systems
Set technical direction for enterprise-wide AI architecture and unify 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 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
8+ years of 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 VP level
Experience developing AI and ML algorithms or technologies, including LLM inference, similarity search, VectorDBs, guardrails, and memory
Experience optimizing training and inference software for hardware utilization, latency, throughput, and cost
Experience building agentic AI systems and workflows
Excellent communication and presentation skills
Track record 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
Remote work eligibility