Machine Learning Engineer 4 - Machine Learning Experiences (MLX)

Capital One • San Jose, CA

1d

About The Position

Capital One is seeking a Machine Learning Engineer to help drive AI transformations and scale production machine learning models. The role spans machine learning engineering, operations, modeling, and data engineering, and involves collaborating with Product and Data Science teams as well as cross-functional Agile teams. The engineer will build models and software components for business problems, inform infrastructure decisions based on knowledge of machine learning methods, develop cloud-based solutions, and maintain production models and services. The role also emphasizes secure, well-governed code and models, Responsible and Explainable AI, and continuous integration, deployment, testing, and monitoring.

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 and operating production services in the cloud (AWS, GCP, or Azure), including 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.
• 3+ years of experience optimizing machine learning 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 common issues such as underfitting and overfitting.
• 3+ years of experience designing, implementing, and scaling production-ready data pipelines for training and evaluating machine learning models.
• 1+ years 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 for real-world 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.
• Write and test application code, develop and validate machine learning models, and automate testing and deployment to solve complex problems.
• Collaborate on 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 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, for 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.
• 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