Machine Learning Engineer

Capital One

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

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, with a strong focus on cloud-based architectures and large-scale distributed systems. Proficient in programming languages and frameworks essential for machine learning, along with a commitment to best practices in software development and model governance.

Highest-signal resume keywords

Machine Learning Model DevelopmentCloud Services (AWS, GCP, Azure)Python ProgrammingLarge-Scale Distributed Systems (Spark, Ray)Continuous Integration/Continuous Deployment (CI/CD)

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

KubernetesCloud-Based ArchitecturesData PipelinesMonitoring ToolsTesting Automation

Certifications & Qualifications

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

Industry Keywords

Responsible AIExplainable AISupervised LearningUnsupervised LearningReinforcement Learning

Tech Stack

Tools & technologies

AWSAzureCloudDistributed SystemsGoogle Cloud PlatformJavaKubernetesNumpyOpen SourcePandasPythonPyTorchRayScalaScikit-LearnSparkTensorflowC++Go

About the role

Key responsibilities & impact

Design, build, and deliver machine learning models and components solving real-world business problems
Build and scale multi-tenant platforms for large-scale ML model training and serving
Inform ML infrastructure decisions using modeling techniques and issues including model, 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 with Product, Data Science, and cross-functional Agile teams
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
Ensure code is well-managed, models are risk-governed, 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 programming experience 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 and Kubernetes
Capital One will consider sponsoring a new qualified applicant for employment authorization
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 planning
Preferred: 5+ years working with supervised, semi-supervised, unsupervised, and reinforcement learning; regression, classification, and clustering; RNNs, CNNs, LSTMs, and Transformers; 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 varied audiences

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
Reasonable accommodations for applicants who require them