Machine Learning Engineer 4

Capital One’s Risk Tech team is hiring a Machine Learning Engineer 4 to build and deploy proprietary risk management solutions using advanced AI. The role focuses on end-to-end machine learning engineering, including model development, production operations, and the supporting infrastructure and delivery pipelines.

Location

• Richmond, VA (onsite)

Compensation

• USD 179,400 - 204,700 per year (Richmond, VA)

Responsibilities

• Conduct a range of ML engineering activities, including designing, building, and/or delivering ML models and components that address real business problems, in collaboration with Product and Data Science teams
• Guide ML infrastructure decisions using understanding of ML modeling concepts, including model choice, data and feature selection, model training, hyperparameter tuning, dimensionality, bias/variance considerations, and validation
• Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment
• Work on a cross-functional Agile team to create and enhance software enabling big data and ML applications
• Retrain, maintain, and monitor models in production
• Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale
• Construct optimized data pipelines to provide inputs for ML models
• Apply continuous integration and continuous deployment best practices, including test automation and monitoring, to support successful deployment of ML models and application code
• Maintain well-managed code to reduce vulnerabilities, ensure risk-governed model practices, and follow best practices in Responsible and Explainable AI
• Use programming languages such as Python, Scala, or Java

Minimum Qualifications

• 4+ years of experience programming with Python, Java, Golang, or C++
• 4+ years of ML experience using industry-standard frameworks PyTorch or Tensorflow and libraries Pandas, NumPy, Scikit-learn
• 4+ years of experience working with and operating large-scale distributed systems such as Spark and Ray to prepare AI/ML data
• 2+ years of experience deploying and operating ML solutions in production, operating production services in the cloud (AWS, GCP, Azure), and using Kubernetes to manage large-scale containerized ML systems
• Bachelor’s Degree or higher in Computer Science, Machine Learning, or a related quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Engineering)

Technology Stack

• Python, Scala, Java, Golang, C++
• PyTorch, Tensorflow
• Pandas, NumPy, Scikit-learn
• Spark, Ray
• AWS, GCP, Azure
• Kubernetes

Benefits

Capital One offers a comprehensive, competitive, and inclusive set of health, financial, and other benefits to support total well-being.