Machine Learning Engineer, Level 5

Capital One

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

Posted 9/22/2026full-timeUnited StatesMid-LevelSenior💰 $209,000 - $286,200 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 applying CI/CD best practices and optimizing ML algorithms while ensuring responsible and explainable AI practices.

Highest-signal resume keywords

Machine Learning Model DevelopmentCloud Services (AWS, GCP, Azure)Python ProgrammingKubernetes ManagementCI/CD Best Practices

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningPythonJavaPyTorchTensorFlowPandasNumPyScikit-learnSparkRay

Soft Skills

CommunicationCollaborationProblem-Solving

Tools & Technologies

KubernetesCI/CD ToolsCloud PlatformsAgile Methodologies

Certifications & Qualifications

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

Industry Keywords

Machine Learning TechniquesData PipelinesModel EvaluationResponsible AIExplainable AI

Tech Stack

Tools & technologies

AWSAzureCloudDistributed SystemsGoogle Cloud PlatformJavaKubernetesNumpyOpen SourcePandasPythonPyTorchRayScalaScikit-LearnSparkTensorflowC++Go

About the role

Key responsibilities & impact

Design, build, and deliver ML models and components solving real-world business problems
Collaborate with Product and Data Science teams
Build and scale multi-tenant platforms for large-scale ML model training and serving
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, and automate tests and deployment
Collaborate in cross-functional Agile teams to create and enhance 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 CI/CD best practices, test automation, and monitoring for ML model and application deployment
Manage code to reduce vulnerabilities and govern models from a risk perspective
Apply Responsible and Explainable AI best practices
Use programming languages such as 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 6 years of experience programming with Python, Java, Golang, or C++
At least 6 years of Machine Learning experience using PyTorch or TensorFlow and Pandas, NumPy, and Scikit-learn
At least 6 years of experience using and operating large-scale distributed systems such as Spark or Ray to prepare AI/ML data
At least 4 years of experience deploying and operating Machine Learning solutions in production
At least 4 years of experience operating production cloud services using AWS, GCP, or Azure
At least 4 years of experience using Kubernetes to manage large-scale containerized ML software systems
Ability to communicate complex technical and machine learning concepts clearly to a variety of audiences
Master's or Doctoral Degree in Computer Science, Electrical Engineering, Mathematics, or related field (preferred)
5+ years of experience optimizing ML algorithms, configurations, and infrastructure (preferred)
5+ years of experience with software development best practices including source control, testing, code reviews, and CI/CD (preferred)
5+ years of experience building resilient software solutions with pre-production testing, advanced deployment techniques, monitoring, alarms, and incident response planning (preferred)
5+ years of experience with Machine Learning techniques, model types, architectures, training concepts, and model evaluation (preferred)
5+ years of experience designing, implementing, and scaling production-ready data pipelines for training and evaluating ML models (preferred)
ML industry impact through conference presentations, papers, blog posts, open source contributions, or patents (preferred)
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