✦ Luna Orbit — AI & Machine Learning

Sr. Lead, Machine Learning Engineer (Enterprise Platforms Technology)

at Capital One Financial

📍 2 Locations Unknown 💰 $229K – $262K USD / year Posted March 17, 2026
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Position Details

Salary $229K – $262K USD / year
Type Not Specified
Experience senior
Exp. Years 8+ years
Education Not specified
Category AI & Machine Learning

About this role

This role involves designing and deploying large-scale machine learning applications at Capital One, focusing on scalable, high-performance AI systems.

Key Responsibilities

  • Design scalable ML models
  • Deploy ML applications
  • Optimize data pipelines
  • Lead AI initiatives
  • Ensure high availability

Technical Overview

The technical environment includes distributed computing platforms, Python, Scala, Java, and AI frameworks, with an emphasis on production ML systems and data pipelines.

Ideal Candidate

The ideal candidate is a senior ML engineer with over 8 years of experience in distributed computing, Python, Scala, and Java, capable of designing scalable machine learning systems and leading AI initiatives.

Must-Have Skills

Machine LearningPythonScalaJavaDistributed Computing

Nice-to-Have Skills

Model DeploymentModel OptimizationData PipelinesAI

Tools & Platforms

PythonScalaJavaDistributed Computing PlatformsML Frameworks

Required Skills

Machine LearningML EngineeringDistributed ComputingPythonScalaJavaModel DeploymentModel OptimizationData PipelinesAI

Hard Skills

Machine LearningML EngineeringDistributed ComputingPythonScalaJavaModel DeploymentModel OptimizationData PipelinesAI

Soft Skills

LeadershipCollaborationProblem-solvingInnovationTechnical Expertise

Industry & Role

Industry Financial Services / Technology
Job Function Leading enterprise-scale machine learning engineering projects.
Role Subtype ML Engineer
Tech Domains Python, Scala, Java, Distributed Computing

Clearance & Visa

Clearance Required None
Visa Sponsorship No

Keywords for Your Resume

machine learningML engineeringdistributed computingPythonScalaJavamodel deploymentmodel optimizationdata pipelinesAIML modelsbig dataproduction systemsscaling MLhigh availabilityMachine LearningDistributed ComputingModel DeploymentModel OptimizationData PipelinesML Models

Deal Breakers

Lack of experience in distributed computing, No background in ML engineering, Unfamiliar with production ML systems

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