Machine Learning Engineer 4, Manager

Capital One

. Design, build, and deliver machine learning models and components solving real-world business problems .

Posted 9/23/2026full-timeUnited StatesMid-LevelSenior💰 $179,400 - $245,600 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing, building, and deploying machine learning models and solutions, with a strong focus on cloud-based architectures and large-scale distributed systems. Proficient in programming languages such as Python and Java, and experienced in applying best practices for software development and ML model management.

Highest-signal resume keywords

Machine Learning ExperiencePython ProgrammingCloud Production Services (AWS, GCP, Azure)Kubernetes ManagementData Pipeline Development

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningPythonJavaGolangC++PyTorchTensorFlowPandasNumPyScikit-learn

Tools & Technologies

AWSGCPAzureKubernetesSparkRay

Certifications & Qualifications

Bachelor's Degree in Computer ScienceMaster's or Doctoral Degree (Preferred)

Industry Keywords

Machine Learning ModelsBig Data ApplicationsAgile TeamsContinuous IntegrationContinuous DeploymentResponsible AIExplainable AI

Tech Stack

Tools & technologies

AWSAzureCloudDistributed SystemsGoogle Cloud PlatformJavaKubernetesNumpyPandasPythonPyTorchRayScalaScikit-LearnSparkTensorflowC++Go

About the role

Key responsibilities & impact

Design, build, and deliver machine learning models and components solving real-world business problems
Collaborate with Product and Data Science teams
Inform ML infrastructure decisions using knowledge of modeling techniques, data, feature selection, training, tuning, dimensionality, bias/variance, and validation
Write and test application code
Develop and validate ML models
Automate tests and deployment
Collaborate on cross-functional Agile teams to create and enhance big data and ML applications
Retrain, maintain, and monitor production models
Leverage or build cloud-based architectures, technologies, and platforms for optimized ML models at scale
Construct optimized data pipelines feeding ML models
Apply continuous integration and continuous deployment practices, including test automation and monitoring
Ensure code is well-managed, models are risk-governed, and ML follows Responsible and Explainable AI best practices

Requirements

What you’ll need

Bachelor's Degree or higher in Computer Science, Machine Learning, or a related quantitative field
At least 4 years of experience programming with Python, Java, Golang, or C++
At least 4 years of Machine Learning experience using PyTorch or TensorFlow and Pandas, NumPy, and Scikit-learn
At least 4 years of experience using and operating large-scale distributed systems such as Spark or Ray for AI/ML data
At least 2 years of experience deploying and operating ML solutions in production and cloud production services using AWS, GCP, or Azure
At least 2 years of experience using Kubernetes to manage large-scale containerized ML software systems
Ability to use Python, Scala, or Java
Preferred: Master's or Doctoral Degree in Computer Science, Electrical Engineering, Mathematics, or related field
Preferred: 3+ years optimizing ML algorithms, configurations, and infrastructure
Preferred: 3+ years following software development best practices including source control, testing, code reviews, and CI/CD
Preferred: 3+ years building resilient software solutions with pre-production testing, advanced deployment techniques, monitoring, alarms, and incident response planning
Preferred: 3+ years working with ML techniques, model types, architectures, training concepts, and model evaluation
Preferred: 3+ years designing, implementing, and scaling production-ready data pipelines for training and evaluating ML models
Preferred: 1+ years as a technical lead developing ML solutions
Preferred: Authored or co-authored a paper on an ML technique, model, or proof of concept
Capital One will not sponsor a new applicant for employment authorization or provide immigration-related support

Benefits

Comp & perks

Performance-based incentive compensation, including cash bonus(es) and/or long-term incentives (LTI)
Comprehensive, competitive health benefits
Financial and other benefits supporting total well-being
Reasonable accommodation support for applicants with disabilities