Machine Learning Engineer, Level 5

Capital One

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

Posted 9/23/2026full-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 at scale, utilizing programming languages such as Python and Java, and leveraging cloud platforms like AWS, GCP, or Azure. Proficient in managing large-scale distributed systems and applying best practices in CI/CD, testing, and monitoring for optimized ML performance.

Highest-signal resume keywords

Machine Learning Model DevelopmentCloud-Based Architecture DeploymentLarge-Scale Distributed Systems ManagementCI/CD and Test AutomationPython Programming

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningPythonJavaPyTorchTensorFlowPandasNumPyScikit-learnKubernetesSpark

Soft Skills

CommunicationCollaborationProblem-Solving

Tools & Technologies

AWSGCPAzureDaskXGBoost

Certifications & Qualifications

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

Industry Keywords

Machine Learning InfrastructureData Pipeline OptimizationResponsible AIExplainable AIAgile Methodologies

Tech Stack

Tools & technologies

AWSAzureCloudDistributed SystemsGoogle Cloud PlatformJavaKubernetesNumpyOpen SourcePandasPythonPyTorchRayScalaScikit-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
Build and scale multi-tenant platforms for large-scale ML model training and serving
Inform ML infrastructure decisions through model, data, feature selection, training, tuning, dimensionality, bias/variance, and validation expertise
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
Build or leverage cloud-based architectures and platforms to deliver optimized ML models at scale
Construct optimized data pipelines feeding ML models
Apply CI/CD, test automation, and monitoring best practices
Manage code to reduce vulnerabilities, govern models from a risk perspective, and apply 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 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 or Machine Learning data
At least 4 years of experience deploying and operating Machine Learning solutions in production and operating production services in the cloud using AWS, GCP, or Azure
At least 4 years of experience using Kubernetes to manage large-scale containerized Machine Learning software systems
Master's or Doctoral Degree in Computer Science, Electrical Engineering, Mathematics, or related field (preferred)
Preferred experience optimizing ML algorithms, configurations, and infrastructure
Preferred experience with software development best practices including source control, testing, code reviews, and CI/CD
Preferred experience building resilient software solutions with pre-production testing, advanced deployment techniques, monitoring, alarms, and incident response plans
Preferred experience with supervised, semi-supervised, unsupervised, and reinforcement learning; regression, classification, and clustering; RNNs, CNNs, LSTMs, and Transformers; training and model evaluation concepts
Preferred experience designing, implementing, and scaling production-ready data pipelines for training and evaluating ML models
Preferred on-the-job experience with Dask or XGBoost
ML industry impact through conference presentations, papers, blog posts, open source contributions, or patents
Ability to communicate complex technical and machine learning concepts clearly to a variety of audiences
Applicants may require employment authorization sponsorship

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
Capital One is an equal opportunity employer committed to non-discrimination
Drug-free workplace