Machine Learning Engineer 4

Capital One’s Risk Tech team is building and deploying proprietary risk management solutions using modern AI. This onsite role in Richmond, VA focuses on designing, operationalizing, and monitoring machine learning models, along with the supporting infrastructure and production workflows that enable them at scale.

As a Machine Learning Engineer 4, you will contribute across the lifecycle: developing model components, implementing data and CI/CD pipelines, and ensuring models are maintained and governed for Responsible and Explainable AI.

What you’ll do

• Conduct a range of ML engineering work, including designing, building, and/or delivering ML models and components to address real business problems in collaboration with Product and Data Science
• Make ML infrastructure decisions based on practical experience with modeling techniques and common challenges, including model and data/feature selection, training, hyperparameter tuning, dimensionality, bias/variance, and validation
• Solve complex technical problems by writing and testing application code, developing and validating ML models, and automating tests and deployment
• Work in a cross-functional Agile team to build and improve software supporting big data and ML applications
• Retrain, maintain, and monitor models in production
• Leverage and/or build cloud-based architectures and platforms to deliver optimized ML models at scale
• Build optimized data pipelines that supply ML models
• Apply continuous integration and continuous deployment practices, including test automation and monitoring, to support reliable deployments of ML models and application code
• Support strong engineering practices by managing code to reduce vulnerabilities, ensuring risk-governed model practices, and implementing Responsible and Explainable AI best practices
• Use programming languages including Python, Scala, or Java

Minimum qualifications

• 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 including Pandas, NumPy, Scikit-learn
• At least 4 years of experience using and operating large scale distributed systems such as Spark or Ray to prepare AI/ML data
• At least 2 years deploying and operating ML solutions in production, including operating production services in the cloud (AWS, GCP, Azure) and using Kubernetes to manage large scale containerized ML systems

Tools and technologies

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

Compensation

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

Benefits

• Comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being