Senior Machine Learning Engineer; Foundations

Senior Machine Learning Engineer (Intelligent Foundations & Experiences) at Capital One summary:

The Senior Machine Learning Engineer at Capital One is responsible for designing, developing, and product ionizing scalable machine learning models and systems in an Agile environment. The role involves collaborating with cross-functional teams, utilizing cloud platforms, and ensuring the deployment, monitoring, and maintenance of models while adhering to best practices in Responsible and Explainable AI. Candidates should have extensive programming experience, knowledge of ML frameworks, and a strong background in building data pipelines and ML infrastructure.

As a Capital One Machine Learning Engineer (MLE), you'll be part of an Agile team dedicated to product ionizing machine learning applications and systems ’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.

Intelligent Foundations & Experiences (IFX) is a powerful collective of horizontal technology organizations that are driving Capital One’s real-time intelligent future. Together with our partners in the Enterprise and across lines of business, we deliver broad-reaching technical solutions and advance state-of-the-art science to help every Capital One associate and our 100+M customers succeed.

What you’ll do in the role:

• The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this role, you'll be expected to perform many ML engineering activities, including one or more of the following:

• Design, build, and/or deliver ML models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams.

• Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues, including choice of model, 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 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 Qualifications:

• Bachelor’s degree

• At least 4 years of experience programming with Python, Scala, or Java (Internship experience does not apply)

• At least 3 years of experience designing and building data-intensive solutions using distributed computing

• At least 2 years of on-the-job experience with an industry recognized ML frameworks (scikit-learn, PyTorch, Dask, Spark, or Tensor Flow)

• At least 1 year of experience product ionizing, monitoring, and maintaining models

Preferred Qualifications:

• 1+ years of experience building, scaling, and optimizing ML systems

• 1+ years of experience with data gathering and preparation for ML models

• 2+ years of experience developing performant, resilient, and maintainable code

• Experience developing and deploying ML solutions in a public cloud such as AWS, Azure, or Google Cloud Platform

• Master's or…