Machine Learning Engineer 4

Capital One • McLean, VA

3d • $179,400 - $245,600

About The Position

Capital One is seeking a Machine Learning Engineer passionate about using open-source frameworks, advanced algorithms, and emerging technologies to help drive AI transformations and scale production models. The role spans machine learning operations, modeling, and data engineering, and involves working with Product and Data Science teams as well as cross-functional Agile teams. The engineer will build and deliver ML models and components for business problems, make informed ML infrastructure decisions, develop and test application code, automate testing and deployment, and maintain and monitor production models. The role also involves cloud-based ML architectures, optimized data pipelines, CI/CD practices, risk governance, and responsible and explainable AI. Programming languages may include Python, Scala, or Java.

The position requires a bachelor's degree or higher in a quantitative or computer science-related field and substantial experience with programming, machine learning frameworks, distributed data systems, production ML, cloud services, and Kubernetes. A master's or doctoral degree, deeper experience in ML optimization and production software practices, technical leadership, and ML research authorship are preferred.

The full-time annual salary range depends on work location: $197,300–$225,100 in McLean, Virginia; $215,200–$245,600 in New York, New York; and $179,400–$204,700 in Philadelphia, Pennsylvania; Richmond, Virginia; or Wilmington, Delaware. The role is also eligible for performance-based incentive compensation, which may include cash bonuses and/or long-term incentives. Capital One describes its benefits as a comprehensive, competitive, and inclusive set of health, financial, and other benefits; eligibility varies by employment and management status.

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 a cloud environment (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, model accuracy evaluation, and diagnosing and addressing 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/or deliver machine learning models and components that solve real-world business problems in collaboration with Product and Data Science teams.
• Inform ML infrastructure decisions using knowledge of modeling techniques and issues, including model, 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 ML models, and automating tests and deployment.
• Collaborate as part of a cross-functional Agile team to create and enhance software for state-of-the-art big data and ML 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, for ML 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 varies by employment status and management level.
• Eligible 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