Senior Lead Machine Learning Engineer

Capital One

Senior Lead Machine Learning Engineer building scalable ML systems for Capital One, an information-based technology banking company. Designing models, cloud architectures, production pipelines, and responsible AI solutions.

Posted 8/10/2026full-timeChicago • Illinois, Virginia • 🇺🇸 United StatesSenior💰 $209,000 - $262,400 per yearWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in designing and building machine learning models and data-intensive solutions, with a strong focus on cloud-based architectures and continuous integration practices. Proven ability to lead teams and communicate complex technical concepts effectively.

Highest-signal resume keywords

Machine Learning Model DevelopmentPython ProgrammingCloud-Based Architecture (AWS, Azure, Google Cloud)Data Pipeline ConstructionTeam Leadership in ML Solutions

ATS Keywords

Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills

Machine LearningData PreparationModel Training and TuningContinuous IntegrationContinuous DeploymentDistributed ComputingML Frameworks (scikit-learn, PyTorch, TensorFlow)Application Code DevelopmentAutomated TestingRisk Management

Soft Skills

Clear CommunicationCollaborationTeam Management

Tools & Technologies

PythonScalaJavaBig Data TechnologiesAgile Methodologies

Certifications & Qualifications

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

Industry Keywords

Responsible AIExplainable AIML InfrastructureProduction ModelsData-Intensive Solutions

Tech Stack

Tools & technologies

AWSAzureCloudGoogle Cloud PlatformJavaOpen SourcePythonPyTorchScalaScikit-LearnSparkTensorflow

About the role

Key responsibilities & impact

Design, build, and/or deliver machine learning models and components solving real-world business problems
Collaborate with Product and Data Science teams
Make ML infrastructure decisions involving model choice, data and feature selection, training, tuning, dimensionality, bias/variance, and validation
Write and test application code, develop and validate ML models, and automate tests and deployment
Collaborate on a cross-functional Agile team developing big data and ML applications
Retrain, maintain, and monitor production models
Build or leverage 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, test automation, and monitoring
Manage code to reduce vulnerabilities and govern models from a risk perspective
Apply Responsible and Explainable AI best practices
Use Python, Scala, or Java

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 programming experience 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
Experience developing and deploying ML solutions in a public cloud such as AWS, Azure, or Google Cloud Platform
4+ years of on-the-job experience with an industry-recognized ML framework such as scikit-learn, PyTorch, Dask, Spark, or TensorFlow
3+ years of experience developing performant, resilient, and maintainable code
3+ years of experience with data gathering and preparation for ML models
3+ years of people management experience
ML industry impact through conference presentations, papers, blog posts, open source contributions, or patents
3+ years of experience building production-ready data pipelines feeding ML models
Ability to communicate complex technical concepts clearly to varied audiences
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 who require them