Machine Learning Engineer, Senior Manager, IC

Capital One

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

Posted 10/27/2026full-timeUnited StatesSenior💰 $229,900 - $286,200 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 services like AWS, GCP, or Azure. Proficient in building scalable 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

Soft Skills

CommunicationCollaborationProblem-Solving

Tools & Technologies

KubernetesCI/CDCloud-Based ArchitecturesAgile Methodologies

Certifications & Qualifications

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

Industry Keywords

Machine Learning TechniquesModel EvaluationResponsible AIExplainable AIDistributed Systems

Tech Stack

Tools & technologies

AWSAzureCloudDistributed SystemsGoogle Cloud PlatformJavaKubernetesNumpyOpen SourcePandasPythonPyTorchRayScalaScikit-LearnSparkTensorflowC++Go

About the role

Key responsibilities & impact

Design, build, and/or deliver ML models and components that solve real-world business problems in collaboration with Product and Data Science teams
Build and scale massive multi-tenant platforms for large-scale ML model training and serving
Inform ML infrastructure decisions using knowledge of modeling techniques, data and feature selection, training, hyperparameter tuning, dimensionality, bias/variance, and validation
Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment
Collaborate as part of a cross-functional Agile team 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 to feed ML models
Apply continuous integration and continuous deployment best practices, including test automation and monitoring
Ensure code is well-managed, models are well-governed from a risk perspective, and ML follows 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, or 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 cloud services using AWS, GCP, or Azure
At least 4 years of experience using Kubernetes to manage large-scale containerized Machine Learning software systems
Preferred: Master's or Doctoral Degree in Computer Science, Electrical Engineering, Mathematics, or related field
Preferred: 5+ years optimizing ML algorithms, configurations, and infrastructure
Preferred: 5+ years following software development best practices including source control, testing, code reviews, and CI/CD
Preferred: 5+ years building resilient software solutions with pre-production testing, advanced deployment techniques, monitoring, alarms, and incident response plans
Preferred: 5+ years working with Machine Learning techniques, model types, model architectures, training concepts, and model evaluation
Preferred: 5+ years designing, implementing, and scaling production-ready data pipelines for training and evaluating ML models
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
Capital One will consider sponsoring a new qualified applicant for employment authorization for this position

Benefits

Comp & perks

Performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI)
Comprehensive, competitive health, financial and other benefits supporting total well-being
Reasonable accommodations for applicants who require them