Senior Lead Machine Learning Engineer

Senior Lead Machine Learning Engineer at Capital One responsible for productionizing ML applications at scale, focusing on architecture, design, and deployment within Agile teams.

Responsibilities

• Design and deliver ML models and components that address real business needs, collaborating with Product and Data Science teams.
• Guide ML infrastructure choices by applying knowledge of modeling techniques, including model selection, data handling, feature engineering, training, hyperparameter tuning, dimensionality, bias-variance considerations, and validation.
• Develop and validate code and ML models, automate tests, and support deployment to production to solve complex problems.
• Work within a cross-functional Agile team to build software powering advanced big data and ML applications.
• Retrain, maintain, and monitor models in production to ensure performance and reliability.
• Leverage cloud-based architectures and platforms to deliver scalable ML solutions.
• Construct efficient data pipelines that feed ML models.
• Apply CI/CD best practices, test automation, and monitoring to ensure reliable deployment of models and code.
• Maintain secure, well-governed code and ensure ML practices align with Responsible and Explainable AI standards.
• Work with Python, Scala, or Java to implement solutions.

Requirements

• Bachelor’s Degree
• Minimum 8 years designing and building data-intensive solutions on distributed computing platforms (no internship credits).
• Minimum 4 years programming in Python, Scala, or Java.
• Minimum 3 years building, scaling, and optimizing ML systems.
• Minimum 2 years leading teams that develop ML solutions.

Technologies

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

Benefits

• Health benefits
• Financial benefits
• Performance-based incentives (cash bonuses and long-term incentives)

Preferred Qualifications

• Master’s or doctoral degree in computer science, electrical engineering, mathematics, or a related field
• Experience developing and deploying ML solutions in a public cloud such as AWS, Azure, or Google Cloud Platform
• 4+ years using industry-recognized ML frameworks such as scikit-learn, PyTorch, Dask, Spark, or TensorFlow
• 3+ years delivering performant, resilient, and maintainable code
• 3+ years of data gathering and preparation for ML models
• 3+ years of people management experience
• Contributions to ML industry impact through conference talks, papers, blogs, open source, or patents
• 3+ years building production-ready data pipelines for ML models
• Ability to communicate complex technical concepts clearly to diverse audiences