✦ Luna Orbit — AI & Machine Learning

Lead Machine Learning Engineer (Enterprise Platforms Technology)

at Capital One Financial

📍 2 Locations Unknown 💰 $197K – $225K USD / year Posted April 15, 2026
Salary $197K – $225K USD / year
Type Full-Time
Experience lead
Exp. Years 6+ years; 4+ years programming; 2+ years ML systems
Education Bachelor's Degree
Category AI & Machine Learning

Lead machine learning engineering efforts to productionize and scale ML applications within enterprise platforms. You will design ML architectures, build and validate models, automate testing and deployment, and ensure models are retrained, monitored, and governed with Responsible and Explainable AI.

  • Design, build, and deliver ML models and components
  • Develop and validate ML models while writing and testing application code
  • Automate tests and deployment using continuous integration and continuous deployment
  • Retrain, maintain, and monitor models in production
  • Construct optimized data pipelines and ensure Responsible and Explainable AI governance

This role centers on ML engineering using Python, Scala, or Java, including model development and validation (hyperparameter tuning, bias/variance, and validation). It requires building cloud-based architectures and optimized data pipelines, deploying ML through CI/CD with test automation and monitoring, and operating models with governance aligned to Responsible and Explainable AI.

The ideal candidate is a lead machine learning engineer with 6+ years designing and building data-intensive solutions using distributed computing, and 4+ years programming with Python, Scala, or Java. They have 2+ years building and scaling ML systems in production, including continuous integration/continuous deployment, data pipeline development, and Responsible and Explainable AI practices.

Bachelor's Degreeat least 6 years of experience designing and building data-intensive solutions using distributed computingat least 4 years of experience programming with PythonScalaor Javaat least 2 years of experience buildingscalingand optimizing ML systemsmachine learning application codehigh availabilitymodel monitoringResponsible and Explainable AI
3+ years of experience building production-ready data pipelines that feed ML models3+ years of on-the-job experience with an industry recognized ML framework such as scikit-learnPyTorchDaskMaster's or Doctoral Degree in computer scienceelectrical engineeringmathematicsor a similar field
scikit-learnPyTorchDask
machine learning engineeringproductionizing machine learning applications and systemsmachine learning architectural designhigh availabilitymodel and application codehyperparameter tuningdimensionalitybias/variancevalidationautomated tests and deploymentretrain maintain monitor models in productioncloud-based architecturesdata pipelinescontinuous integrationcontinuous deploymenttest automationmonitoringvulnerability reductionResponsible and Explainable AIPythonScalaJavadistributed computingAgilescikit-learnPyTorchDask
machine learning engineeringproductionizing machine learning applications and systems at scalemachine learning architectural designmodel and application code development and reviewhigh availabilityperformancehyperparameter tuningdimensionalitybias/variancevalidationautomating tests and deploymentcross-functional Agile teamretraining modelsmaintain models in productionmonitor models in productioncloud-based architecturesdata pipelinescontinuous integrationcontinuous deploymenttest automationmonitoringvulnerability reductionResponsible and Explainable AIgoverned models from a risk perspectiveprogramming languages like PythonScalaor Javadistributed computing
cross-functional collaborationteamworkcommunicationproblem-solvingcontinuous learningengineering rigorAgile collaboration
Industry Banking
Job Function Lead end-to-end ML engineering for scalable, governed, production ML systems
Role Subtype ML Engineer
Tech Domains Python, Java, Amazon Web Services, Kubernetes, AI & Machine Learning, Data Engineering
Lead Machine Learning EngineerMachine Learning Engineer (MLE)Enterprise Platforms Technologyproductionizing machine learning applicationsmachine learning architectural designhigh availabilitymodel and application coderetrainmaintainmonitor models in productioncloud-based architecturesdata pipelinescontinuous integrationcontinuous deploymenttest automationmonitoringResponsible and Explainable AIPythonScalaJavadistributed computinghyperparameter tuningvalidationbias/variancescikit-learnPyTorchDaskAgileMachine Learning Engineer

Bachelor's Degree required, At least 6 years designing/building data-intensive solutions using distributed computing required, At least 4 years programming with Python, Scala, or Java required, At least 2 years building/scaling/optimizing ML systems required

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