Lead Machine Learning Engineer Role At Capital One
The world of banking technology is rapidly evolving, with machine learning (ML) leading the charge. Capital One is on the lookout for a Lead Machine Learning Engineer (MLE) who will play a crucial role in productionizing ML applications and systems at scale as part of an Agile team. This position offers a unique opportunity to participate in the detailed technical design, development, and implementation of machine learning applications using both existing and emerging technology platforms.
The role primarily focuses on machine learning architectural design. The MLE will develop and review model and application code, ensuring high availability and performance of the company’s ML applications. The opportunity to continuously learn and apply the latest innovations and best practices in machine learning engineering is an added bonus.
Responsibilities and Duties
The MLE role overlaps with many disciplines, such as Operations, Modeling, and Data Engineering. A range of ML engineering activities is expected, including the following:
- Design and build ML models and components that solve real-world business problems, in collaboration with the Product and Data Science teams.
- Inform ML infrastructure decisions using an understanding of ML modeling techniques and issues, including model selection, data and feature selection, model training, hyperparameter tuning, dimensionality, bias/variance, and validation.
- 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 that enables state-of-the-art big data and ML applications.
- Retrain, maintain, and monitor models in production.
- Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale.
- Construct optimized data pipelines to feed ML models.
- Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure the successful deployment of ML models and application code.
- Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI.
- Use programming languages like Python, Scala, or Java.
Basic and Preferred Qualifications
Capital One is seeking an individual with a Bachelor’s Degree and at least six years of experience in designing and building data-intensive solutions using distributed computing. Additionally, the candidate should have at least four years of programming experience with Python, Scala, or Java, and at least two years of experience building, scaling, and optimizing ML systems.
While these are the basic qualifications, preferred qualifications include a Master’s or Doctoral Degree in computer science, electrical engineering, mathematics, or a similar field. More years of experience with data pipelines, ML frameworks, developing performant code, data gathering, team leadership, and industry best practices are also preferred. Experience with public cloud platforms and complex data pipelines for ML models is highly desirable.
Compensation and Benefits
The annual full-time salary for this role in McLean, VA, ranges between $197,300 and $225,100. Capital One also offers a comprehensive, competitive, and inclusive set of health, financial, and other benefits that support your total well-being.
For more details on the position and how to apply, click Here.
