Machine Learning Engineer 5 (Senior Manager, IC)

Capital One • McLean, VA

2d • $229,900 - $262,400

About The Position

Capital One is seeking a Machine Learning Engineer to help drive AI transformations and scale production models. The role spans operations, modeling, and data engineering, and involves delivering machine learning solutions to real-world business problems in collaboration with Product and Data Science teams. The engineer will help build large-scale, multi-tenant training and serving platforms, apply knowledge of modeling methods and infrastructure needs, and develop cloud-based systems for optimized machine learning at scale.

The position also involves creating and maintaining data pipelines, writing and testing application code, developing and validating models, automating tests and deployments, and retraining, maintaining, and monitoring production models. The engineer will work in a cross-functional Agile environment and follow practices for continuous integration and deployment, model governance, risk reduction, and Responsible and Explainable AI. Relevant programming languages include Python, Scala, and Java.

Requirements

• Bachelor's degree or higher in Computer Science, Machine Learning, or a related quantitative field such as Statistics, Economics, Operations Research, Analytics, Mathematics, or Engineering.
• At least 6 years of programming experience with Python, Java, Golang, or C++.
• At least 6 years of machine learning experience using industry-standard frameworks such as PyTorch or TensorFlow and libraries such as Pandas, NumPy, and Scikit-learn.
• At least 6 years of experience using and operating large-scale distributed systems such as Spark or Ray to prepare AI or machine learning data.
• At least 4 years of experience deploying and operating machine learning solutions in production, operating production services in the cloud (AWS, GCP, or Azure), and using Kubernetes to manage large-scale containerized machine learning systems.

Nice To Haves

• Master's or doctoral degree in Computer Science, Electrical Engineering, Mathematics, or a related field.
• 5 or more years of experience optimizing machine learning algorithms, configurations, and infrastructure.
• 5 or more years of experience following software development best practices, including source control, testing, code reviews, and CI/CD.
• 5 or more years of experience building resilient software solutions with pre-production testing, advanced deployment techniques, monitoring, alarms, and incident response plans.
• 5 or more years of experience with machine learning techniques, model types and architectures, training concepts, model evaluation, and diagnosing issues such as underfitting and overfitting.
• 5 or more years of experience designing, implementing, and scaling production-ready data pipelines for training and evaluating machine learning models.
• Machine learning industry impact through conference presentations, papers, blog posts, open-source contributions, or patents.
• Ability to clearly communicate complex technical and machine learning concepts to a variety of audiences.

Responsibilities

• Design, build, and deliver machine learning models and components for business problems in collaboration with Product and Data Science teams.
• Build and scale multi-tenant platforms for large-scale machine learning model training and serving.
• Inform machine learning infrastructure decisions using knowledge of modeling techniques, data and feature selection, model training, hyperparameter tuning, dimensionality, bias and variance, and validation.
• Write and test application code, develop and validate machine learning models, and automate tests and deployment.
• Collaborate on a cross-functional Agile team to develop and enhance software for big data and machine learning applications.
• Retrain, maintain, and monitor production models.
• Leverage or build cloud-based architectures, technologies, and platforms to deliver optimized machine learning models at scale.
• Construct optimized data pipelines to feed machine learning models.
• Apply continuous integration and continuous deployment practices, including test automation and monitoring, to deploy models and application code successfully.
• Manage code to reduce vulnerabilities, govern models from a risk perspective, and apply Responsible and Explainable AI practices.

Benefits

• Comprehensive, competitive, and inclusive health, financial, and other benefits supporting total well-being.
• Eligibility for performance-based incentive compensation, which may include cash bonuses and/or long-term incentives.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior