Apply

Ready to go for it?

AI Apply speeds things up—apply directly if you prefer.

FREE ACCESS
5,000–10,000 jobs/day
Scoutfield Logo

See all jobs on Scoutfield

Search thousands of fresh jobs every day.

Discover
  • Fresh listings
  • Fast filters
  • No subscription required
Create a free account and start exploring right away.
Capital One

AI Engineer 5 – MLXT

Capital One

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

Posted 9/28/2026full-timeUnited StatesMid-LevelSenior💰 $229,900 - $286,200 per yearWebsite

Core Competencies

Role fit
Core Competencies

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

Demonstrates expertise in developing and optimizing AI and ML algorithms, with a strong focus on cost, latency, and throughput in cloud environments. Proven ability to lead technical teams and mentor engineers while ensuring compliance with AI engineering standards.

Highest-signal resume keywords
AI And ML Algorithm DevelopmentPython ProgrammingCloud Deployment (AWS, Google Cloud, Azure)AI System OptimizationTeam Leadership And Mentorship

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 Software DevelopmentFoundation Model TrainingLLM InferenceMulti-Agent WorkflowsModel EvaluationExperimentationGovernanceObservabilityCost-Performance GovernanceDynamic Inference Strategies
Soft Skills
MentoringCollaborationTechnical Vision Contribution
Tools & Technologies
AWS UltraclustersHugging FaceVector DatabasesPyTorchCUDAJavaGoScalaC++C#
Industry Keywords
Ethical AI Deployment StandardsExplainabilityFairnessHuman-In-The-Loop ReviewModel Compression

Tech Stack

Tools & technologies
AWSAzureCloudJavaPythonPyTorchScalaC++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, LLM inference, agents and multi-agent workflows, similarity search, guardrails, model evaluation, experimentation, governance, and observability
  • Leverage open-source and SaaS AI technologies including AWS Ultraclusters, Hugging Face, vector databases, and PyTorch
  • Invent and introduce foundation model optimization techniques to improve scalability, cost, latency, and throughput of production AI systems
  • Contribute to the technical vision and long-term roadmap of foundational AI systems
  • Design, implement, and optimize multi-model orchestration pipelines integrating LLMs, vector search, and domain-specific models
  • Establish and lead cost-performance governance reviews, tracking GPU utilization, model throughput, and inference cost efficiency
  • Lead team design councils or design review boards to ensure technical consistency and compliance with AI engineering standards
  • Mentor Principal- and Manager-level AI engineers and elevate organizational technical maturity

Requirements

What you’ll need
  • Bachelor's degree in Computer Science, AI, Electrical Engineering, Computer Engineering, or related field plus at least 6 years of experience developing AI and ML algorithms or technologies; or Master's degree in one of these fields plus at least 4 years of such experience
  • At least 6 years of experience programming with Python, Go, Scala, CUDA, or Java
  • Experience leading development of AI systems with cost, latency, throughput, and accuracy tradeoff decisions
  • 7+ years of experience deploying scalable and responsible AI solutions on cloud platforms such as AWS, Google Cloud, Azure, or equivalent private cloud
  • Experience designing, developing, delivering, and supporting complex AI systems
  • Experience developing AI and ML algorithms or technologies using Python, C++, C#, Java, CUDA, or Golang
  • Experience optimizing training and inference software for hardware utilization, latency, throughput, and cost
  • Experience building agentic AI systems and workflows
  • Experience architecting and integrating rule-based, retrieval-augmented, and generative components into unified production pipelines
  • Experience defining and enforcing ethical AI deployment standards, including explainability, fairness, and human-in-the-loop review processes
  • Demonstrated ability to balance model performance and operational cost through dynamic inference strategies and model compression
  • 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
  • Employment authorization sponsorship may be considered for a new qualified applicant