Machine Learning Engineer 4 – Manager, IC

Capital One

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

Posted 10/6/2026full-timeUnited StatesMid-LevelSenior💰 $215,200 - $245,600 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 programming languages and frameworks essential for developing and optimizing ML applications.

Highest-signal resume keywords

Machine Learning Model DevelopmentPython ProgrammingCloud Services (AWS, GCP, Azure)Kubernetes 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 PlatformsData PipelinesAutomated Testing Tools

Certifications & Qualifications

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

Industry Keywords

Machine Learning TechniquesData ScienceFeature SelectionModel EvaluationResponsible 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 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
Automate tests 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 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 (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 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 services in the cloud (AWS, GCP, Azure)
At least 2 years of experience using Kubernetes to manage large-scale containerized Machine Learning software systems
No employer-sponsored immigration or work-authorization support available for new applicants
Preferred: 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 planning
Preferred: 3+ years of experience with Machine Learning techniques, model types, architectures, training concepts, and model evaluation
Preferred: 3+ years of experience designing, implementing, and scaling production-ready data pipelines for training and evaluating ML models
Preferred: 1+ year of experience 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 health, financial, and other benefits supporting total well-being