Lead Machine Learning Engineer, Python, GoLang, AWS

Capital One

Capital One Lead ML Engineer designing, deploying, and monitoring production machine learning systems at scale. Building cloud architectures, data pipelines, and responsible AI solutions with cross-functional teams.

Posted 9/8/2026full-timeMcLean • California, Massachusetts, New York, Virginia • 🇺🇸 United StatesSenior💰 $197,300 - $245,600 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in machine learning architectural design, model development, and optimization, with a strong focus on building scalable and efficient ML systems. Proficient in leveraging cloud-based technologies and implementing best practices in responsible AI and continuous integration.

Highest-signal resume keywords

Machine Learning Architectural DesignPython ProgrammingBuilding Production-Ready Data PipelinesCloud-Based ArchitecturesContinuous Integration and Deployment

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine Learning ApplicationsModel DevelopmentData Pipeline ConstructionHyperparameter TuningModel ValidationDistributed ComputingApplication Code DevelopmentAutomated TestingPerformance OptimizationFeature Selection

Soft Skills

CollaborationProblem SolvingContinuous LearningCross-Functional TeamworkCommunication

Tools & Technologies

Cloud TechnologiesAgile MethodologiesML FrameworksData Science ToolsBig Data Technologies

Industry Keywords

Responsible AIExplainable AIData-Intensive SolutionsMachine Learning EngineeringModel Governance

Tech Stack

Tools & technologies

CloudJavaPythonScala

About the role

Key responsibilities & impact

Participate in the detailed technical design, development, and implementation of machine learning applications using existing and emerging technology platforms
Focus on machine learning architectural design
Develop and review model and application code
Ensure high availability and performance of machine learning applications
Design, build, and/or deliver ML models and components that solve real-world business problems in collaboration with Product and Data Science teams
Inform ML infrastructure decisions using understanding of modeling techniques and issues, including model choice, data and feature selection, training, hyperparameter tuning, dimensionality, bias/variance, and validation
Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment
Collaborate with a cross-functional Agile team to create and enhance software enabling big data and ML applications
Retrain, maintain, and monitor models in production
Leverage or build cloud-based architectures, technologies, and platforms to deliver optimized ML models at scale
Construct optimized data pipelines to feed ML models
Apply continuous integration and continuous deployment practices, including test automation and monitoring
Ensure code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and ML follows Responsible and Explainable AI best practices
Continuously learn and apply the latest innovations and best practices in machine learning engineering

Requirements

What you’ll need

Bachelor’s Degree
At least 6 years of experience designing and building data-intensive solutions using distributed computing (Internship experience does not apply)
At least 4 years of experience programming with Python, Scala, or Java
At least 2 years of experience building, scaling, and optimizing ML systems
Preferred: Master’s or Doctoral Degree in computer science, electrical engineering, mathematics, or a similar field
Preferred: 3+ years of experience building production-ready data pipelines that feed ML models
No agencies please

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 with disabilities