Machine Learning Engineer 5

Capital One • McLean, VA

10d • $229,900 - $262,400

About The Position

Capital One is seeking a Machine Learning Engineer to build and scale production machine learning models and infrastructure. The role is part of a specialized group responsible for decisioning technology that supports customers throughout their credit journey. The team standardizes and streamlines the development, deployment, and monitoring of complex machine learning models, using a modern technology stack focused on Python and Kubernetes to support real-time workloads for millions of customers.

The position spans machine learning operations, modeling, and data engineering. The engineer will collaborate with Product, Data Science, and cross-functional Agile teams to deliver machine learning models, platforms, software, and data pipelines. The work includes deploying and operating models in cloud environments, maintaining production services, applying CI/CD practices, and ensuring responsible, explainable, and well-governed AI. The role is based on the listed compensation information for McLean, Virginia; other locations use their applicable pay ranges.

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 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 and operating cloud production services on AWS, GCP, or Azure, 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.
• At least 5 years of experience optimizing machine learning algorithms, configurations, and infrastructure.
• At least 5 years of experience following software development best practices, including source control, testing, code reviews, and CI/CD.
• At least 5 years of experience building resilient software solutions with pre-production testing, advanced deployment techniques, monitoring, alarms, and incident response plans.
• At least 5 years of experience with machine learning techniques, model types and architectures, training concepts, and methods for evaluating model accuracy and diagnosing issues such as underfitting and overfitting.
• At least 5 years of experience designing, implementing, and scaling production-ready data pipelines for training and evaluating machine learning models.
• Machine learning industry impact demonstrated through conference presentations, papers, blog posts, open-source contributions, or patents.
• Ability to clearly communicate complex technical and machine learning concepts to varied audiences.

Responsibilities

• Design, build, and deliver machine learning models and components to solve 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.
• Use knowledge of machine learning modeling techniques and issues to inform infrastructure decisions, including model, data, and feature selection; training; hyperparameter tuning; dimensionality; bias and variance; and validation.
• Solve problems by writing and testing application code, developing and validating machine learning models, and automating tests and deployment.
• Collaborate on cross-functional Agile teams to create and enhance software for big data and machine learning applications.
• Retrain, maintain, and monitor models in production.
• 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 support successful deployment of models and application code.
• Manage code to reduce vulnerabilities, govern models from a risk perspective, and follow Responsible and Explainable AI best practices.
• Use programming languages such as Python, Scala, or Java.

Benefits

• Comprehensive health, financial, and other benefits supporting total well-being.
• Eligibility for performance-based incentive compensation, potentially including cash bonuses and/or long-term incentives.

What This Job Offers

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