Machine Learning Engineer

Capital One

. Design, build, and deliver Machine Learning models and components solving real-world business problems .

Posted 10/8/2026full-timeUnited StatesMid-LevelSenior💰 $179,400 - $225,100 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and deploying Machine Learning models, leveraging cloud-based architectures and large-scale distributed systems. Proficient in programming with Python and utilizing ML frameworks such as PyTorch and TensorFlow for real-world applications.

Highest-signal resume keywords

Machine Learning Model DevelopmentCloud Services (AWS, GCP, Azure)Python ProgrammingKubernetes ManagementContinuous Integration and Deployment (CI/CD)

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningPythonJavaPyTorchTensorFlowPandasNumPyScikit-learnSparkRay

Soft Skills

CollaborationProblem SolvingCommunicationAgile Methodologies

Tools & Technologies

KubernetesCloud-Based ArchitecturesData PipelinesMonitoring Tools

Certifications & Qualifications

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

Industry Keywords

Machine Learning TechniquesModel GovernanceResponsible AIExplainable AIProduction Systems

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 based on modeling techniques, data, feature selection, training, tuning, dimensionality, bias/variance, and validation
Write and test application code, develop and validate ML models, and automate testing 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 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 and ensure model governance and Responsible and Explainable AI practices
Use programming languages including Python, Scala, or Java

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 libraries including Pandas, NumPy, and Scikit-learn
At least 4 years of experience using and operating large-scale distributed systems such as Spark or Ray to prepare AI or Machine Learning data
At least 2 years of experience deploying and operating Machine Learning solutions in production and operating production cloud services using AWS, GCP, or Azure
At least 2 years of experience using Kubernetes to manage large-scale containerized Machine Learning software systems
No new employment authorization sponsorship or immigration-related support available
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 Machine Learning techniques, model types, model 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

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
Equal opportunity employer committed to non-discrimination
Reasonable accommodations for applicants who require them