Machine Learning Engineer 4 – Intelligent Foundations and Experiences

Capital One

. Design, build, and/or deliver ML models and components solving real-world business problems in collaboration with Product and Data Science teams .

Posted 9/23/2026full-timeUnited StatesMid-LevelSenior💰 $215,200 - $245,600 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and deploying Machine Learning models and solutions, leveraging programming languages such as Python and Java, and utilizing cloud platforms like AWS, GCP, or Azure. Proficient in building optimized data pipelines and applying best practices in continuous integration and deployment.

Highest-signal resume keywords

Machine Learning ExperiencePython ProgrammingCloud 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 LearningPythonJavaPyTorchTensorFlowPandasNumPyScikit-learnSparkRay

Tools & Technologies

KubernetesCI/CDAgile MethodologiesCloud-Based ArchitecturesAutomated Testing

Certifications & Qualifications

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

Industry Keywords

Machine Learning TechniquesModel EvaluationData ScienceBig Data ApplicationsResponsible AI

Tech Stack

Tools & technologies

AWSAzureCloudDistributed SystemsGoogle Cloud PlatformJavaKubernetesNumpyPandasPythonPyTorchRayScalaScikit-LearnSparkTensorflowC++Go

About the role

Key responsibilities & impact

Design, build, and/or deliver ML models and components solving real-world business problems in collaboration with Product and Data Science teams
Inform ML infrastructure decisions using knowledge of modeling techniques, data and feature selection, training, hyperparameter tuning, dimensionality, bias/variance, and validation
Write and test application code, develop and validate ML models, and automate tests and deployment
Collaborate on cross-functional Agile teams to create and enhance software enabling big data and ML applications
Retrain, maintain, and monitor models in production
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, 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 (Statistics, Economics, Operations Research, Analytics, Mathematics, Engineering)
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/ML data
At least 2 years of experience deploying and operating Machine Learning solutions in production and operating production services in the cloud (AWS, GCP, Azure)
At least 2 years of experience using Kubernetes to manage large-scale containerized ML software systems
No new applicant 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 plans
Preferred: 3+ years working with Machine Learning 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 using industry best practices, patterns, and automation
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 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