Machine Learning Engineer 4 (Python, AWS, SQL, GenAI) (Enterprise Platforms Technology)

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Capital One's Enterprise Platforms Technology team is hiring a Machine Learning Engineer 4 to build and scale production ML models and platforms. The role sits within the Marketing and Messaging team, delivering hyper-personalized omnichannel experiences across owned and paid AdTech channels. You will design ML models, construct data pipelines, and deploy resilient cloud-based ML systems in collaboration with Product and Data Science teams.

• Design, build, and deliver ML models and components that solve real-world business problems
• Inform ML infrastructure decisions based on modeling techniques, data and feature selection, training, and validation
• Write and test application code, develop and validate ML models, and automate tests and deployment
• Retrain, maintain, and monitor models in production
• Build cloud-based architectures and optimized data pipelines to feed ML models at scale
• Apply CI/CD best practices, test automation, and monitoring for ML deployments
• Ensure code security, model governance, and adherence to Responsible and Explainable AI practices

• Bachelor's Degree or higher in Computer Science, Machine Learning, or a related quantitative field
• At least 4 years of experience programming with Python, Java, Golang, or C++
• At least 4 years of ML experience with PyTorch or TensorFlow and libraries such as Pandas, NumPy, and Scikit-learn
• At least 4 years of experience operating large-scale distributed systems (Spark, Ray) for AI/ML data
• At least 2 years of experience deploying and operating ML solutions in production on AWS, GCP, or Azure
• Experience using Kubernetes to manage large-scale containerized ML systems

• Master's or Doctoral Degree in Computer Science, Electrical Engineering, Mathematics, or a related field
• 3+ years optimizing ML algorithms, configurations, and infrastructure
• 3+ years following software development best practices including source control, testing, code reviews, and CI/CD
• 3+ years building resilient software with pre-production testing, blue/green and gradual deployments, monitoring, and incident response
• 3+ years with ML techniques, model architectures (RNNs, CNNs, LSTMs, Transformers), and training concepts
• 3+ years designing and scaling production data pipelines for ML training and evaluation
• 1+ years as a technical lead developing ML solutions
• Authored or co-authored a paper on an ML technique, model, or proof of concept