Machine Learning Engineer 4

Capital One is hiring a Machine Learning Engineer 4 in Richmond, VA (onsite) to build and deploy proprietary risk management solutions using AI.

Responsibilities

• Perform ML engineering work, including designing, building, and/or delivering ML models and components for real-world business problems in partnership with Product and Data Science
• Guide ML infrastructure decisions using knowledge of modeling techniques and tradeoffs, including model selection, data and feature selection, training, hyperparameter tuning, dimensionality, bias/variance, and validation
• Address complex problems through application code development and testing, ML model development and validation, and automation of tests and deployment
• Collaborate in a cross-functional Agile team to create and improve software that supports big data and ML applications
• Retrain, maintain, and monitor ML models in production
• Leverage or build cloud-based architectures, technologies, and platforms to deliver optimized ML models at scale
• Build optimized data pipelines to supply ML models
• Apply continuous integration and continuous deployment best practices, including test automation and monitoring, for successful deployment of ML models and application code
• Manage code to reduce vulnerabilities, ensure risk-governed models, and apply best practices for Responsible and Explainable AI
• Use programming languages such as Python, Scala, or Java

Requirements

• 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 programming experience with Python, Java, Golang, or C++
• At least 4 years of Machine Learning experience using industry standard frameworks PyTorch or Tensorflow and libraries Pandas, NumPy, Scikit-learn
• At least 4 years using and operating large scale distributed systems such as Spark and 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

Technology Stack

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

Location

• Richmond, VA (onsite)

Salary

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

Benefits

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