Machine Learning Engineer 4

Capital One • New York, NY

4d • $197,300 - $245,600

About The Position

Capital One is seeking a Machine Learning Engineer to help drive AI transformation and scale production models. The role combines activities across operations, modeling, and data engineering, and involves building machine learning models and components for business problems in collaboration with Product and Data Science teams. The engineer will contribute to infrastructure decisions informed by machine learning methods and modeling considerations, develop and test application code and models, automate testing and deployment, and work with a cross-functional Agile team to deliver big-data and machine-learning applications. The position also involves maintaining and monitoring production models, using or building cloud-based platforms to deliver optimized models at scale, constructing data pipelines, applying CI/CD practices, and supporting secure, governed, responsible, and explainable AI.

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 such as 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 machine-learning 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 machine-learning 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 (one-box, blue/green, gradual dial-up), monitoring, alarms, and incident response plans.
• At least 3 years of experience with machine-learning techniques, model types, architectures, training concepts, model accuracy evaluation, and diagnosing and addressing 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 machine-learning models.
• At least 1 year of experience as a technical lead developing machine-learning solutions using industry best practices, patterns, and automation.
• Authored or co-authored a paper on a machine-learning technique, model, or proof of concept.

Responsibilities

• Design, build, and deliver machine learning models and components that address business problems in collaboration with Product and Data Science teams.
• Inform machine learning 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 machine learning models, and automating 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 production models.
• Leverage or build cloud-based architectures, technologies, and platforms to deliver optimized machine learning models at scale.
• Construct optimized data pipelines to feed machine learning 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 apply Responsible and Explainable AI practices.
• Use programming languages such as Python, Scala, or Java.

Benefits

• Comprehensive, competitive, and inclusive 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