Machine Learning Engineer, Level 5

Capital One

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

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 and deploying Machine Learning models and solutions, leveraging cloud-based architectures and large-scale distributed systems. Proficient in programming with Python and utilizing ML frameworks such as PyTorch and TensorFlow for optimized model training and evaluation.

Highest-signal resume keywords

Machine Learning ExperiencePython ProgrammingCloud Services (AWS, GCP, Azure)Kubernetes ManagementCI/CD Practices

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningPythonJavaPyTorchTensorFlowPandasNumPyScikit-learnSparkRay

Soft Skills

Communication

Tools & Technologies

KubernetesAWSGCPAzureDaskXGBoost

Certifications & Qualifications

Bachelor's DegreeMaster's DegreeDoctoral Degree

Industry Keywords

Machine Learning ModelsData PipelinesAgile TeamsResponsible AIExplainable AI

Tech Stack

Tools & technologies

AWSAzureCloudDistributed SystemsGoogle Cloud PlatformJavaKubernetesNumpyOpen SourcePandasPythonPyTorchRayScalaScikit-LearnSparkTensorflowC++Go

About the role

Key responsibilities & impact

Design, build, and deliver ML models and components solving real-world business problems with Product and Data Science teams
Build and scale 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, 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 big-data and ML software
Retrain, maintain, and monitor production models
Leverage or build cloud-based architectures, technologies, and platforms for optimized ML models at scale
Construct optimized data pipelines feeding ML models
Apply CI/CD, test automation, and monitoring practices for successful deployment
Manage code to reduce vulnerabilities, govern models from a risk perspective, and follow 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 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 cloud services using AWS, GCP, or Azure
At least 4 years of experience using Kubernetes to manage large-scale containerized Machine Learning software systems
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
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 ML techniques, model types, architectures, training concepts, and model evaluation
Preferred: 5+ years designing, implementing, and scaling production-ready data pipelines for training and evaluating ML models
Preferred: 4+ years of on-the-job experience with Dask or XGBoost
Preferred: ML industry impact through conference presentations, papers, blog posts, open source contributions, or patents

Benefits

Comp & perks

Performance-based incentive compensation, which may include cash bonuses and/or long-term incentives (LTI)
Comprehensive, competitive, and inclusive health, financial, and other benefits supporting total well-being