Machine Learning Engineer 4, Python, AWS, SQL, GenAI

Capital One

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

Posted 9/21/2026full-timeUnited StatesMid-LevelSenior💰 $179,400 - $245,600 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and deploying Machine Learning models and solutions, utilizing programming languages such as Python and Java, and leveraging cloud platforms like AWS, GCP, or Azure. Proficient in building optimized data pipelines and applying best practices in continuous integration and deployment within Agile environments.

Highest-signal resume keywords

Machine Learning ExperiencePython ProgrammingCloud Services (AWS, GCP, Azure)Data Pipeline DevelopmentContinuous Integration/Continuous Deployment (CI/CD)

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningPythonJavaPyTorchTensorFlowPandasNumPyScikit-learnSparkKubernetes

Tools & Technologies

Cloud PlatformsAgile MethodologiesCI/CD ToolsDistributed Systems

Certifications & Qualifications

Bachelor's Degree in Computer ScienceMaster's or Doctoral Degree in Related Field

Industry Keywords

Machine Learning TechniquesModel EvaluationData ScienceResponsible AIExplainable AI

Tech Stack

Tools & technologies

AWSAzureCloudDistributed SystemsGoogle Cloud PlatformJavaKubernetesNumpyPandasPythonPyTorchRayScalaScikit-LearnSparkTensorflowC++Go

About the role

Key responsibilities & impact

Design, build, and/or deliver ML models and components solving real-world business problems
Collaborate with Product and Data Science teams
Inform ML infrastructure decisions using knowledge of modeling techniques and issues
Write and test application code, develop and validate ML models, and automate tests and deployment
Collaborate in a cross-functional Agile team to create and enhance software for 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 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 (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 industry standard frameworks PyTorch or Tensorflow and libraries (Pandas, NumPy, Scikit-learn)
At least 4 years of experience using and operating large scale distributed systems (Spark, Ray) to prepare AI/ML 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) and using Kubernetes to manage large scale containerized ML software systems
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, CI/CD, etc. preferred
3+ years of experience building resilient software solutions with pre-production testing, advanced deployment techniques, monitoring, alarms, and incident response plans preferred
3+ years of experience working with Machine Learning techniques, model types, model 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+ years of experience as a technical lead developing ML solutions using industry best practices, patterns, and automation preferred
Authored/co-authored a paper on an ML technique, model, or proof of concept preferred
Capital One will not sponsor a new applicant for employment authorization or offer immigration-related support for this position

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
Equal opportunity employer committed to non-discrimination
Reasonable accommodations for applicants who require them