Machine Learning Engineer - Capital One Software (Remote)

$117,400 - $134,000/Yr

Capital One - McLean, VA

Full-time - Mid Level

Remote - McLean, VA

Credit Intermediation and Related Activities

About the position

As a Machine Learning Engineer at Capital One, you will play a crucial role in building and delivering machine learning models and components that address real-world business challenges. You will collaborate with product and data science teams to design and implement ML solutions, optimize data pipelines, and ensure the successful deployment of models in a cloud environment. This position offers the opportunity to work on innovative projects within a cross-functional Agile team, contributing to the development of state-of-the-art big data and ML applications.

Responsibilities

• Design, build, and deliver ML models and components that solve real-world business problems.
• Collaborate with Product and Data Science teams to inform ML infrastructure decisions.
• Write and test application code, develop and validate ML models, and automate tests and deployment.
• Collaborate as part of a cross-functional Agile team to enhance software for big data and ML applications.
• Retrain, maintain, and monitor models in production.
• Leverage or build cloud-based architectures to deliver optimized ML models at scale.
• Construct optimized data pipelines to feed ML models.
• Implement continuous integration and continuous deployment best practices for ML models and application code.
• Ensure code is well-managed to reduce vulnerabilities and models are well-governed from a risk perspective.

Requirements

• Bachelor's degree
• At least 2 years of experience designing and building data-intensive solutions using distributed computing
• 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)

Nice-to-haves

• Experience developing and deploying ML solutions in a public cloud such as AWS, Azure, or Google Cloud Platform
• 1+ years of experience working with large code bases in a team environment
• 1+ years of experience with distributed file systems or multi-node database paradigms
• Contributed to open source ML software
• 1+ years of experience building production-ready data pipelines that feed ML models

Benefits

• Comprehensive health benefits
• Financial benefits including performance-based incentives
• Inclusive workplace environment
• Support for total well-being