Sr. Lead, Machine Learning Engineer (Enterprise Platforms Technology)

Capital One's Enterprise Platforms Technology (EPTech) team offers a path to productionize machine learning at scale, with a focus on architectural design, development, and governance of ML models and infrastructure. This onsite role in McLean, VA comes with a competitive annual compensation range of USD 229,900 to 262,400, plus a benefits package designed to support health, financial security, and total well-being.

Responsibilities

• Design, build, and deliver ML models and components that address real business needs, partnering with Product managers and Data Science teams.
• Inform ML infrastructure decisions with a solid understanding of modeling techniques, including model choice, data and feature selection, training, hyperparameter tuning, dimensionality, bias vs. variance, and validation.
• Solve complex problems by writing and validating application code, developing ML models, and automating tests and deployment processes.
• Collaborate within a cross functional Agile team to create and enhance software that enables state of the art big data and ML applications.
• Retrain, maintain, and monitor models once they are in production.
• Leverage or build cloud-based architectures, technologies, and platforms to deliver optimized ML models at scale.
• Construct optimized data pipelines to feed ML models.
• Adopt continuous integration and continuous deployment practices, including test automation and monitoring, to ensure successful deployment of ML models and application code.
• Ensure code quality and governance to reduce vulnerabilities, maintain risk-aware practices for models, and follow Responsible and Explainable AI principles.
• Work with Python, Scala, or Java to implement solutions.

Requirements

• Bachelor’s degree; Master’s or doctoral degree in computer science, electrical engineering, mathematics, or a related field.
• At least 8 years of experience designing and building data-intensive solutions using distributed computing (internship experience does not apply).
• At least 4 years of experience programming with Python, Scala, or Java.
• At least 3 years of experience building, scaling, and optimizing ML systems.
• At least 2 years of experience leading teams developing ML solutions.
• Experience developing and deploying ML solutions in a public cloud such as AWS, Azure, or Google Cloud Platform.
• 4+ years of on-the-job experience with an industry recognized ML framework such as scikit-learn, PyTorch, Dask, Spark, or TensorFlow.
• 3+ years of experience developing performant, resilient, and maintainable code.
• 3+ years of experience with data gathering and preparation for ML models.
• 3+ years of people management experience.
• ML industry impact through conference presentations, papers, blog posts, open source contributions, or patents.
• 3+ years of experience building production-ready data pipelines that feed ML models.
• Ability to communicate complex technical concepts clearly to a variety of audiences.

Technologies

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

Benefits

• Health benefits
• Financial benefits
• Incentives (cash bonus and/or long-term incentives)
• Other benefits that support total well-being