Machine Learning Engineer 5

Capital One • McLean, VA

8d • $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 guides customers through their credit journey. The team standardizes how complex models are built, deployed, and monitored, using a modern technology stack focused on Python and Kubernetes to support millions of customers in real time.

The position spans machine learning engineering, operations, modeling, and data engineering. The engineer works with Product and Data Science teams and within cross-functional Agile teams to create and improve software and platforms for large-scale machine learning applications. Responsibilities include developing models and components, building distributed training and serving platforms, maintaining production models, constructing data pipelines, and applying cloud, deployment, monitoring, governance, and responsible AI practices.

The role requires a bachelor's degree or higher in a quantitative or computer science-related field, at least six years of programming and machine learning experience, and at least four years deploying and operating machine learning solutions in production and cloud environments. A master's or doctoral degree and additional experience in production ML, software engineering, resilient systems, and ML research or industry contributions are preferred. The listed full-time annual salary range for McLean, Virginia is $229,900–$262,400. The role is also eligible for performance-based incentive compensation, which may include cash bonuses and/or long-term incentives.

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 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 production services in a cloud environment such as 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.
• 5+ years of experience optimizing machine learning algorithms, configurations, and infrastructure.
• 5+ years of experience following software development best practices, including source control, testing, code reviews, and CI/CD.
• 5+ years of experience building resilient software solutions with pre-production testing, advanced deployment techniques, monitoring, alarms, and incident response plans.
• 5+ years of experience with machine learning techniques, model types, model architectures, training concepts, and evaluating model accuracy and diagnosing issues such as underfitting and overfitting.
• 5+ 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 communicate complex technical and machine learning concepts clearly to a variety of 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.
• Inform machine learning infrastructure decisions using knowledge of modeling techniques, data and feature selection, training, hyperparameter tuning, dimensionality, bias and variance, and validation.
• Solve complex problems by writing and testing application code, developing and validating machine learning models, and automating tests and deployment.
• Collaborate with cross-functional Agile teams to create and enhance software for big data and machine learning applications.
• Retrain, maintain, and monitor models in production.
• Build or use 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 practices.
• Use programming languages such as Python, Scala, or Java.

Benefits

• Comprehensive health, financial, and other benefits supporting total well-being.
• 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