Lead Machine Learning Engineer

Job Description

As a Capital One Machine Learning Engineer (MLE), you'll be part of an Agile team dedicated to productionizing machine learning applications and systems at scale. You’ll participate in the detailed technical design, development, and implementation of machine learning applications using existing and emerging technology platforms. You’ll focus on machine learning architectural design, develop and review model and application code, and ensure high availability and performance of our machine learning applications. You'll have the opportunity to continuously learn and apply the latest innovations and best practices in machine learning engineering.

What you’ll do in the role:

• Design, build, and/or deliver ML models and components to solve real-world business problems, collaborating with Product and Data Science teams.
• Inform ML infrastructure decisions using your understanding of ML modeling techniques.
• Solve complex problems through 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 software enabling 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 for successful deployment of ML models.
• Ensure code management, model governance, and adherence to Responsible and Explainable AI best practices.
• Use programming languages like Python, Scala, or Java.

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

Preferred Qualifications

• Master's or doctoral degree in computer science, electrical engineering, mathematics, or a similar field
• 3+ years of experience building production-ready data pipelines for ML models
• 3+ years of on-the-job experience with industry recognized ML frameworks
• 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
• Experience with complex data pipelines for ML models and performance evaluation
• ML industry impact through presentations, papers, blog posts, open-source contributions, or patents