Lead Machine Learning Engineer

$201,400 - $229,900/Yr

Capital One - New York, NY

Full-time - Mid Level

New York, NY

Credit Intermediation and Related Activities

About the position

As a Lead 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 work closely with data scientists and other engineers to deploy custom machine learning models and enhance existing workflows, utilizing a variety of technologies and platforms.

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 follow best practices in Responsible and Explainable AI.

Requirements

• Bachelor's degree
• At least 6 years of experience designing and building data-intensive solutions using distributed computing
• At least 4 years of experience programming with Python, Scala, or Java
• At least 2 years of experience building, scaling, and optimizing ML systems

Nice-to-haves

• Master's or doctoral degree in computer science, electrical engineering, mathematics, or a similar field
• 3+ years of experience building production-ready data pipelines that feed ML models
• 3+ years of experience with an industry recognized ML framework such as scikit-learn, PyTorch, Dask, Spark, or TensorFlow
• 2+ years of experience developing performant, resilient, and maintainable code
• 2+ years of experience with data gathering and preparation for ML models
• 2+ years of people leader experience
• 1+ years of experience leading teams developing ML solutions using industry best practices
• Experience developing and deploying ML solutions in a public cloud such as AWS, Azure, or Google Cloud Platform
• Experience designing, implementing, and scaling complex data pipelines for ML models

Benefits

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