Senior Lead Machine Learning Engineer

Capital One

Senior Lead Machine Learning Engineer productionizing scalable machine learning applications for Capital One. Designing ML architectures, models, pipelines, and cloud-based systems.

Posted 9/4/2026full-timeMcLean • California, Massachusetts, New York, Virginia • 🇺🇸 United StatesSenior💰 $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 designing, developing, and implementing machine learning applications, with a strong focus on architectural design, model optimization, and cloud-based technologies. Proficient in managing data pipelines and ensuring high performance and availability of ML systems while adhering to best practices in responsible AI.

Highest-signal resume keywords

Machine Learning Architectural DesignPython ProgrammingCloud-Based Technologies (AWS, Azure, Google Cloud Platform)Model Optimization and ValidationData Pipeline Construction

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningModel DevelopmentHyperparameter TuningContinuous IntegrationContinuous DeploymentData GatheringFeature SelectionScikit-LearnPyTorchTensorFlow

Soft Skills

Team LeadershipCommunication of Technical Concepts

Tools & Technologies

DaskSparkCloud PlatformsAgile Methodologies

Certifications & Qualifications

Bachelor's DegreeMaster's or Doctoral Degree (Preferred)

Industry Keywords

Machine Learning EngineeringData-Intensive SolutionsResponsible AIExplainable AIBig Data Applications

Tech Stack

Tools & technologies

AWSAzureCloudGoogle Cloud PlatformJavaOpen SourcePythonPyTorchScalaScikit-LearnSparkTensorflow

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 solving real-world business problems with Product and Data Science teams
Inform ML infrastructure decisions using modeling techniques and issues including model choice, data and feature selection, training, hyperparameter tuning, dimensionality, bias/variance, and validation
Write and test application code, develop and validate ML models, and automate tests and deployment
Collaborate in a cross-functional Agile team creating and enhancing software for 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 feeding ML models
Apply continuous integration and continuous deployment best practices, including test automation and monitoring
Manage code to reduce vulnerabilities, govern models from a risk perspective, and follow Responsible and Explainable AI best practices
Continuously learn and apply innovations and best practices in machine learning engineering

Requirements

What you’ll need

Bachelor’s Degree
At least 8 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 3 years of experience building, scaling, and optimizing ML systems
At least 2 years of experience leading teams developing ML solutions
Preferred: Master's or Doctoral Degree in computer science, electrical engineering, mathematics, or a similar field
Preferred: Experience developing and deploying ML solutions in AWS, Azure, or Google Cloud Platform
Preferred: 4+ years of on-the-job experience with scikit-learn, PyTorch, Dask, Spark, or TensorFlow
Preferred: 3+ years of experience developing performant, resilient, and maintainable code
Preferred: 3+ years of experience with data gathering and preparation for ML models
Preferred: 3+ years of people management experience
Preferred: ML industry impact through conference presentations, papers, blog posts, open source contributions, or patents
Preferred: 3+ years of experience building production-ready data pipelines that feed ML models
Preferred: Ability to communicate complex technical concepts clearly to a variety of audiences
Preferred: Experience leveraging interactive AI tooling beyond basic code completion
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
Reasonable accommodations for applicants with disabilities
Equal opportunity and non-discrimination commitment
Drug-free workplace