Senior Machine Learning Engineer (Python, Spark, MLOPS)

Capital One - West McLean, VA

Full-time - Mid Level

West McLean, VA

Credit Intermediation and Related Activities

About the position

As a Senior Machine Learning Engineer at Capital One, you will be part of an Agile team focused on productionizing machine learning applications and systems at scale. Your role will involve the technical design, development, and implementation of machine learning applications, ensuring high availability and performance. You will collaborate with cross-functional teams to solve real-world business problems using machine learning techniques and best practices.

Responsibilities

• Design, build, and deliver ML models and components that solve real-world business problems.
• Inform ML infrastructure decisions using understanding of ML modeling techniques and issues.
• Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment.
• Collaborate as part of a cross-functional Agile team to create and 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.
• Leverage continuous integration and continuous deployment best practices to ensure successful deployment of ML models and application code.
• Ensure all code is well-managed to reduce vulnerabilities and that models are well-governed from a risk perspective.

Requirements

• Bachelor's degree
• At least 4 years of experience programming with Python, Scala, or Java
• At least 3 years of experience designing and building data-intensive solutions using distributed computing
• At least 2 years of on-the-job experience with industry recognized ML frameworks (scikit-learn, PyTorch, Dask, Spark, or TensorFlow)
• At least 1 year of experience productionizing, monitoring, and maintaining models

Nice-to-haves

• 1+ years of experience building, scaling, and optimizing ML systems
• 1+ years of experience with data gathering and preparation for ML models
• 2+ years of experience developing performant, resilient, and maintainable code
• Experience developing and deploying ML solutions in a public cloud such as AWS, Azure, or Google Cloud Platform
• Master's or doctoral degree in computer science, electrical engineering, mathematics, or a similar field
• 3+ years of experience with distributed file systems or multi-node database paradigms
• Contributed to open source ML software
• Authored/co-authored a paper on a ML technique, model, or proof of concept
• 3+ years of experience building production-ready data pipelines that feed ML models
• Experience designing, implementing, and scaling complex data pipelines for ML models and evaluating their performance

Benefits

• Comprehensive health benefits
• Financial benefits
• Inclusive set of benefits supporting total well-being