Machine Learning Engineer 5

Capital One • McLean, VA

8d • $209,000 - $286,200

About The Position

Capital One is seeking a Machine Learning Engineer to help drive AI transformations and scale production models. The role combines activities across operations, modeling, and data engineering, and involves building ML models, infrastructure, platforms, and applications that solve business problems. The engineer works collaboratively with Product, Data Science, and cross-functional Agile teams, and contributes to reliable, governed, and responsible ML solutions deployed at scale.

Requirements

• Bachelor's degree or higher in Computer Science, Machine Learning, or a related quantitative field.
• 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 ML software 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 ML 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, model architectures, training concepts, and methods for evaluating accuracy 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 ML models.
• Machine learning industry impact demonstrated through conference presentations, papers, blog posts, open-source contributions, or patents.
• Ability to communicate complex technical and machine learning concepts clearly to varied audiences.

Responsibilities

• Design, build, and deliver machine learning models and components for real-world 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 modeling techniques and issues—including model, data, and feature selection; training; hyperparameter tuning; dimensionality; bias and variance; and validation—to inform ML infrastructure decisions.
• Write and test application code, develop and validate ML models, and automate testing and deployment to solve complex problems.
• Collaborate with cross-functional Agile teams to create 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 ML models at scale.
• Build optimized data pipelines that feed ML 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

• Performance-based incentive compensation, which may include cash bonuses and/or long-term incentives.
• A comprehensive, competitive, and inclusive set of health, financial, and other benefits supporting total well-being.

What This Job Offers

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