Machine Learning Engineer 4

Capital One • New York, NY

3d

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 machine learning engineering, operations, modeling, and data engineering, working with Product and Data Science teams and within cross-functional Agile teams. The engineer will build and deliver models and components for business problems, make informed ML infrastructure decisions, and develop cloud-based systems and data pipelines for optimized models at scale. The role also includes maintaining and monitoring production models, automating testing and deployment, and applying responsible, explainable, secure, and well-governed ML practices.

The position requires at least four years of programming and machine learning experience, including experience with industry-standard frameworks, distributed systems, and production ML solutions in the cloud. A bachelor's degree or higher in computer science, machine learning, or a related quantitative field is required. The position is available in McLean, Virginia, and New York, New York. The stated annual salary range is $197,300–$225,100 in McLean and $215,200–$245,600 in New York. The role may also earn performance-based incentive compensation, including cash bonuses and/or long-term incentives. Capital One does not provide new employment-authorization sponsorship or 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 and operating production services in the cloud (AWS, GCP, or Azure), including 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.
• At least 3 years of experience optimizing ML algorithms, configurations, and infrastructure.
• At least 3 years of experience following software development best practices, including source control, testing, code reviews, and CI/CD.
• At least 3 years of experience building resilient software solutions with pre-production testing, advanced deployment techniques, monitoring, alarms, and incident response plans.
• At least 3 years of experience with machine learning techniques, model types and architectures, training concepts, model evaluation, and diagnosing common issues such as underfitting and overfitting.
• At least 3 years of experience designing, implementing, and scaling production-ready data pipelines for training and evaluating ML models.
• At least 1 year 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 techniques, 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.
• 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 deploy models and application code successfully.
• 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.
• 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