Machine Learning Engineer – Manager

Capital One

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

Posted 11/5/2026full-timeUnited StatesMid-LevelSenior💰 $197,300 - $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 Python, Java, and modern ML frameworks, while applying best practices in CI/CD and responsible AI.

Highest-signal resume keywords

Machine Learning Model DevelopmentCloud-Based Architecture (AWS, GCP, Azure)Kubernetes ManagementPython ProgrammingDistributed Systems (Spark, Ray)

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningPythonJavaGolangPyTorchTensorFlowPandasNumPyScikit-learnKubernetes

Soft Skills

CollaborationProblem-SolvingCommunicationAgile Methodologies

Tools & Technologies

KubeflowDockerCI/CDHyperparameter TuningData Pipelines

Certifications & Qualifications

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

Industry Keywords

Machine Learning SolutionsResponsible AIExplainable AIProduction ServicesBig Data Applications

Tech Stack

Tools & technologies

AWSAzureCloudDistributed SystemsDockerGoogle Cloud PlatformJavaKubernetesNumpyPandasPythonPyTorchRayScalaScikit-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
Make ML infrastructure decisions involving model choice, data, feature selection, training, hyperparameter tuning, dimensionality, bias/variance, and validation
Write and test application code
Develop and validate ML models
Automate testing and deployment
Collaborate on 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 for ML models
Apply CI/CD, test automation, and monitoring practices
Manage code to reduce vulnerabilities and govern models from a risk perspective
Apply Responsible and Explainable AI best practices
Use Python, Scala, or Java
Build and evolve a high-throughput, multi-tenant training platform across classical ML and GenAI workloads
Own the Kubeflow Pipelines v2 application layer on Amazon EKS
Develop internal Python and Go SDKs, custom Kubernetes operators, and developer tooling
Integrate agentic AI into engineering lifecycles
Contribute to upstream open-source Kubeflow

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 and Ray to prepare AI or Machine Learning data
At least 2 years of experience deploying and operating Machine Learning solutions in production
At least 2 years of experience operating production services in AWS, GCP, or Azure
At least 2 years of experience using Kubernetes to manage large-scale containerized Machine Learning software systems
No new applicant employment authorization sponsorship or immigration-related support available
Preferred: Master's or Doctoral Degree in a related field
Preferred: 3+ years designing, training, optimizing, and evaluating modern ML models
Preferred: 3+ years architecting and scaling distributed, end-to-end ML systems
Preferred: 3+ years managing ML workloads using Docker, Kubernetes, and pipeline orchestration tools
Preferred: 3+ years applying software best practices and deploying resilient systems
Preferred: 1+ years as a technical lead developing ML solutions
Preferred: Authored/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 with disabilities