Lead Machine Learning Engineer

Capital One

Lead Machine Learning Engineer productionizing scalable machine learning systems at Capital One. Designing models, data pipelines, cloud architectures, and production deployment practices.

Posted 9/10/2026full-timeMcLean • Texas, Virginia • 🇺🇸 United StatesSenior💰 $197,300 - $225,100 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing, developing, and implementing machine learning applications and systems, with a strong focus on architectural design, model optimization, and cloud-based technologies. Proficient in building data pipelines and applying best practices in machine learning and software development.

Highest-signal resume keywords

Machine Learning Application DevelopmentPython ProgrammingCloud-Based ArchitectureData Pipeline ConstructionAgile Methodologies

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningModel OptimizationData PreparationHyperparameter TuningContinuous IntegrationContinuous DeploymentDistributed ComputingModel ValidationCode ReviewTest Automation

Soft Skills

CollaborationProblem SolvingLeadership

Tools & Technologies

AWSAzureGoogle Cloud PlatformScikit-learnPyTorchDaskSparkTensorFlow

Industry Keywords

Responsible AIExplainable AIBig DataData ScienceMachine Learning Frameworks

Tech Stack

Tools & technologies

AWSAzureCloudGoogle Cloud PlatformJavaOpen SourcePythonPyTorchScalaScikit-LearnSparkTensorflow

About the role

Key responsibilities & impact

Participate in an Agile team dedicated to productionizing machine learning applications and systems at scale
Design, develop, and implement 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 deliver ML models and components solving real-world business problems in collaboration with Product and Data Science teams
Inform ML infrastructure decisions using modeling techniques and considerations such as 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 with a cross-functional Agile team to create and enhance software enabling big data and ML applications
Retrain, maintain, and monitor production models
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 practices, including test automation and monitoring
Manage code to reduce vulnerabilities, govern models from a risk perspective, and apply Responsible and Explainable AI best practices
Use Python, Scala, or Java

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
No employer sponsorship or immigration-related support for employment authorization
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
Preferred: 3+ years of on-the-job experience with an industry-recognized ML framework such as scikit-learn, PyTorch, Dask, Spark, or TensorFlow
Preferred: 2+ years of experience developing performant, resilient, and maintainable code
Preferred: 2+ years of experience with data gathering and preparation for ML models
Preferred: 2+ years of people leader experience
Preferred: 1+ years of experience leading teams developing ML solutions using industry best practices, patterns, and automation
Preferred: Experience developing and deploying ML solutions in AWS, Azure, or Google Cloud Platform
Preferred: Experience designing, implementing, and scaling complex data pipelines for ML models and evaluating their performance
Preferred: ML industry impact through conference presentations, papers, blog posts, open source contributions, or patents
Preferred: Experience leveraging interactive AI tooling beyond basic code completion

Benefits

Comp & perks

Performance-based incentive compensation, which may include cash bonuses and/or long-term incentives (LTI)
Comprehensive, competitive, and inclusive health, financial, and other benefits supporting total well-being
Reasonable accommodations for applicants who require them