Machine Learning Engineer (AI Foundations)

Capital One offers a benefits-forward environment designed to support your total well-being, including health and financial benefits. The role also features a performance-based incentive program that may include cash bonuses and long-term incentives. This on-site position is based in McLean, VA, with a salary range of USD 135,600 to 154,800 per year.

Benefits

• Comprehensive health, financial, and other benefits aimed at supporting your total well-being
• Performance-based incentive compensation that may include cash bonuses and long-term incentives

Responsibilities

• Design, build, and deliver ML models and components that address real business needs, collaborating with Product and Data Science teams
• Inform ML infrastructure decisions with a solid understanding of modeling techniques and tradeoffs, including model choice, data, feature selection, training, hyperparameters, dimensionality, bias/variance, and validation
• Address complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment
• Work within a cross-functional Agile team to create and enhance software for modern big data and ML applications
• Retrain, maintain, and monitor models in production
• Leverage or build cloud-based architectures, technologies, and platforms to deliver optimized ML models at scale
• Construct optimized data pipelines to feed ML models
• Apply CI/CD best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code
• Ensure code quality and governance to reduce vulnerabilities, maintain responsible and explainable AI practices
• Use programming languages such as Python, Scala, or Java

Requirements

• Bachelor’s Degree
• At least 2 years of experience designing and building data-intensive solutions using distributed computing (internship experience does not apply)
• At least 2 years of experience programming with Python, Scala, or Java
• At least 1 year of Machine Learning experience with an industry recognized ML framework (scikit-learn, PyTorch, Dask, Spark, or TensorFlow)

Technologies

Python, Scala, Java, scikit-learn, PyTorch, Dask, Spark, TensorFlow, AWS, Azure, Google Cloud Platform