Machine Learning Engineer 4

Capital One • McLean, VA

1d

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 working with Product and Data Science teams to build machine learning solutions for business problems. The engineer will contribute to ML infrastructure decisions, software development, model development and validation, production operations, and cloud-based systems. The role also emphasizes responsible and explainable AI, model governance, and secure, well-managed code.

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 4 years of programming experience with Python, Java, Golang, or C++.
• At least 4 years of machine learning experience using industry-standard frameworks PyTorch or TensorFlow and libraries such as Pandas, NumPy, and Scikit-learn.
• At least 4 years of experience using and operating large-scale distributed systems such as Spark or Ray to prepare AI or machine learning data.
• At least 2 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.
• 3+ years of experience optimizing ML algorithms, configurations, and infrastructure.
• 3+ years of experience following software development best practices, including source control, testing, code reviews, and CI/CD.
• 3+ years of experience building resilient software solutions with pre-production testing, advanced deployment techniques (one-box, blue/green, gradual dial-up), monitoring, alarms, and incident response plans.
• 3+ 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.
• 3+ years of experience designing, implementing, and scaling production-ready data pipelines for training and evaluating ML models.
• 1+ years of experience as a technical lead developing ML solutions using industry best practices, patterns, and automation.
• Authored or co-authored a paper on an ML technique, model, or proof of concept.

Responsibilities

• Design, build, and deliver machine learning models and components for real-world business problems in collaboration with Product and Data Science teams.
• Inform ML infrastructure decisions using knowledge of modeling techniques, model and data selection, feature selection, training, hyperparameter tuning, dimensionality, bias and variance, and validation.
• Write and test application code, develop and validate ML models, and automate tests and deployment.
• Collaborate on a cross-functional Agile team 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 ML models at scale.
• Construct optimized data pipelines to 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.

Benefits

• Comprehensive 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