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Capital One
Capital One
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Capital One is a financial services and fintech company offering credit cards, auto loans, banking, and savings products. Its work combines financial expertise with technology and digital tools to improve how customers manage money. The company’s careers focus includes building customer-oriented solutions within a diverse and inclusive workforce.
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Senior Machine Learning Engineer San Jose Onsite

Build and operate production machine learning models, data pipelines, and cloud infrastructure at Capital One. Help advance AI capabilities through scalable, governed, and explainable ML solutions.

USUnited StatesCACaliforniaMAMassachusettsNYNew YorkVAVirginia
$197,300 – $245,600 / year
On-siteFull-timeMiddleSeniorEnglish RequiredMachine Learning Engineer
AWSAzureC++CloudDistributed SystemsGoGoogle Cloud PlatformJavaKubernetesNumpyPandasPythonPyTorchRayScalaScikit-LearnSparkTensorflow

Description

  • Design, build, and deliver machine learning models and components for practical business applications
  • Partner with Product and Data Science teams
  • Guide ML infrastructure decisions using modeling methods, data, feature selection, training, tuning, dimensionality, bias and variance, and validation
  • Write and test application code
  • Develop and validate machine learning models
  • Automate testing and deployment workflows
  • Work with a cross-functional Agile team to build and improve big data and ML applications
  • Retrain, maintain, and monitor models running in production
  • Use or develop cloud architectures, technologies, and platforms to deliver optimized ML models at scale
  • Build optimized data pipelines that supply machine learning models
  • Apply continuous integration and continuous deployment practices, including automated testing and monitoring
  • Maintain disciplined code management, model risk governance, and Responsible and Explainable AI practices
  • Program in languages such as Python, Scala, or Java

Requirements

  • Bachelor’s degree or higher in computer science, machine learning, or a related quantitative field
  • At least four years of programming experience with Python, Java, Golang, or C++
  • At least four years of machine learning experience with PyTorch or TensorFlow, Pandas, NumPy, and Scikit-learn
  • At least four years of experience using and operating large-scale distributed systems such as Spark and Ray for AI/ML data preparation
  • At least two years of experience deploying and operating machine learning solutions in production
  • At least two years of experience running production services in the cloud with AWS, GCP, or Azure
  • At least two years of experience using Kubernetes to manage large-scale containerized ML software systems
  • New employment authorization sponsorship and immigration-related support are not available
  • Preferred: Master’s or doctoral degree in computer science, electrical engineering, mathematics, or a related field
  • Preferred: Three or more years optimizing ML algorithms, configurations, and infrastructure
  • Preferred: Three or more years applying software development practices such as source control, testing, code reviews, and CI/CD
  • Preferred: Three or more years building resilient software with pre-production testing, advanced deployment methods, monitoring, alarms, and incident response plans
  • Preferred: Three or more years working with machine learning techniques, model types, architectures, training concepts, and model evaluation
  • Preferred: Three or more years designing, implementing, and scaling production-ready data pipelines for training and evaluating ML models
  • Preferred: One or more years serving as a technical lead for ML solutions using industry practices, patterns, and automation
  • Preferred: Authored or co-authored a paper on an ML technique, model, or proof of concept

Benefits

  • Performance-based incentive compensation may include cash bonuses and/or long-term incentives
  • Comprehensive, competitive, and inclusive health, financial, and other benefits support overall well-being
  • Reasonable accommodations are available for applicants who require them

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