Lead Machine Learning Engineer, Python, AWS, SQL, GenAI

Capital One

Lead ML Engineer building scalable Python, AWS, and GenAI systems for Capital One’s personalized marketing platforms. Designing, deploying, and governing production machine learning applications.

Posted 9/3/2026full-timeNew York City • New York, Virginia • 🇺🇸 United StatesSenior💰 $197,300 - $245,600 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing, building, and optimizing machine learning systems and data pipelines, with a strong focus on cloud-based architectures and CI/CD practices. Proven ability to collaborate with cross-functional teams to deliver high-performance ML applications that solve real-world business problems.

Highest-signal resume keywords

Machine Learning System DesignPython ProgrammingData Pipeline DevelopmentCloud Platform ExperienceAgile Collaboration

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningData EngineeringModel ValidationHyperparameter TuningApplication Code DevelopmentCI/CD PracticesTest AutomationDistributed ComputingPerformance OptimizationData Gathering

Soft Skills

CollaborationLeadershipCommunication

Tools & Technologies

AWSAzureGoogle Cloud PlatformScikit-learnPyTorchDaskSparkTensorFlow

Industry Keywords

Responsible AIExplainable AIBig DataOmnichannel MessagingData-Intensive Solutions

Tech Stack

Tools & technologies

AWSAzureCloudGoogle Cloud PlatformJavaPythonPyTorchScalaScikit-LearnSparkTensorflow

About the role

Key responsibilities & impact

Productionize machine learning applications and systems at scale
Participate in detailed technical design, development, and implementation of ML applications
Focus on ML architectural design and develop and review model and application code
Ensure high availability and performance of ML applications
Design, build, and deliver ML models and components solving real-world business problems in collaboration with Product and Data Science teams
Make ML infrastructure decisions involving 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 testing and deployment
Collaborate with a cross-functional Agile team to create and enhance big data and ML applications
Retrain, maintain, and monitor production models
Leverage or build cloud-based architectures and platforms to deliver optimized ML models at scale
Construct optimized data pipelines feeding ML models
Apply CI/CD practices, test automation, and monitoring for ML model and application deployments
Manage code to reduce vulnerabilities, govern models from a risk perspective, and apply Responsible and Explainable AI best practices
Contribute to the Marketing and Messaging team’s scalable platforms for hyper-personalized omnichannel messages and experiences

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, including cash bonuses and/or long-term incentives (LTI)
Comprehensive health, financial, and other benefits supporting total well-being