Machine Learning Engineer

Capital One

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

Posted 9/22/2026full-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 optimizing data pipelines. Proficient in programming with Python and utilizing frameworks such as PyTorch and TensorFlow for real-world applications.

Highest-signal resume keywords

Machine Learning Model DevelopmentCloud Services (AWS, GCP, Azure)Python ProgrammingData Pipeline ConstructionKubernetes Management

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

KubernetesCI/CDCloud-Based ArchitecturesAutomated Testing

Certifications & Qualifications

Bachelor's Degree in Computer ScienceMaster's or Doctoral Degree in Related Field

Industry Keywords

Machine Learning TechniquesData ScienceBig Data ApplicationsResponsible 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 regarding models, 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 in 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
Ensure code is well-managed to reduce vulnerabilities, models are governed from a risk perspective, and ML follows Responsible and Explainable AI best 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 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/ML 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 ML software systems
Master's or Doctoral Degree in Computer Science, Electrical Engineering, Mathematics, or related field (preferred)
3+ years of experience optimizing ML algorithms, configurations, and infrastructure (preferred)
3+ years of experience following software development best practices including source control, testing, code reviews, and CI/CD (preferred)
3+ years of experience building resilient software solutions with pre-production testing, advanced deployment techniques, monitoring, alarms, and incident response plans (preferred)
3+ years of experience with Machine Learning techniques, model types, architectures, training concepts, and model evaluation and diagnosis (preferred)
3+ years of experience designing, implementing, and scaling production-ready data pipelines for training and evaluating ML models (preferred)
1+ years of experience as a technical lead developing ML solutions (preferred)
Authored/co-authored a paper on a ML technique, model, or proof of concept (preferred)
Capital One will not sponsor a new applicant for employment authorization or provide immigration-related support

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
Reasonable accommodations for applicants who require them