Machine Learning Engineer 4 - Finance Tech

Capital One • McLean, VA

1d • $197,300 - $225,100 • Hybrid

About The Position

Capital One is seeking a Machine Learning Engineer to help drive AI transformations and scale production models. The role is part of Finance Tech, which develops financial technology and data capabilities that support Finance and the broader enterprise through forecasting and resiliency, treasury and trade, accounting and reporting, and core data capabilities.

The Machine Learning Engineer works across disciplines including operations, modeling, and data engineering. The position designs and delivers machine learning models and components for business problems, builds and operates cloud-based ML infrastructure and production services, constructs data pipelines, and supports model retraining, maintenance, and monitoring. The engineer collaborates with Product, Data Science, and cross-functional Agile teams, while applying software engineering, CI/CD, risk governance, and Responsible and Explainable AI practices.

This is a hybrid position. Employees are expected to follow Capital One’s hybrid policy for required on-site presence at their designated office location. The role is eligible for performance-based incentive compensation, which may include cash bonuses and/or long-term incentives. Capital One will not sponsor new applicants for employment authorization or provide immigration-related support for this position.

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 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 such as 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 or more years of experience optimizing ML algorithms, configurations, and infrastructure.
• 3 or more years of experience following software development best practices, including source control, testing, code reviews, and CI/CD.
• 3 or more years of experience building resilient software solutions with pre-production testing, advanced deployment techniques, monitoring, alarms, and incident response plans.
• 3 or more years of experience with machine learning techniques, model types and architectures, training concepts, model evaluation, and diagnosing issues such as underfitting and overfitting.
• 3 or more years of experience designing, implementing, and scaling production-ready data pipelines for training and evaluating ML models.
• 1 or more 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 to solve business problems in collaboration with Product and Data Science teams.
• Inform ML infrastructure decisions using knowledge of modeling methods and issues, including model, data, and feature selection; training; hyperparameter tuning; dimensionality; bias and variance; and validation.
• 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 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.
• Use CI/CD practices, including test automation and monitoring, to deploy ML models and application code successfully.
• Manage code to reduce vulnerabilities, govern models from a risk perspective, and apply 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.
• 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