Senior Manager, Machine Learning Engineer (People Leader)

Capital One • San Francisco, NY

20d

About The Position

Capital One is seeking a Machine Learning Engineer to help drive AI transformations and scale production models. The role combines machine learning engineering with operations, modeling, and data engineering, and includes both hands-on technical work and people leadership. The engineer will collaborate with Product, Data Science, and cross-functional Agile teams to develop, deploy, maintain, and monitor production machine learning models and platforms. The role also involves recruiting, mentoring, and retaining engineering talent and promoting responsible, explainable, and well-governed 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 6 years of programming experience with Python, Java, Golang, or C++.
• At least 6 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 6 years of experience using and operating large-scale distributed systems such as Spark or Ray to prepare AI or machine learning data.
• At least 4 years of experience deploying and operating machine learning solutions in production and operating production services in the cloud (AWS, GCP, or Azure), using Kubernetes to manage large-scale containerized machine learning systems.
• At least 1 year of people leadership experience.

Nice To Haves

• Master's or doctoral degree in Computer Science, Electrical Engineering, Mathematics, or a related field.
• 3 or more years of experience managing and leading a machine learning engineering team.
• 5 or more years of experience optimizing machine learning algorithms, configurations, and infrastructure.
• 5 or more years of experience following software development best practices, including source control, testing, code reviews, and CI/CD.
• 5 or more years of experience building resilient software solutions with pre-production testing, advanced deployment techniques, monitoring, alarms, and incident response plans.
• 5 or more years of experience with machine learning techniques, model types and architectures, training concepts, model evaluation, and diagnosing and addressing common issues such as underfitting and overfitting.
• 5 or more years of experience designing, implementing, and scaling production-ready data pipelines for training and evaluating machine learning models.
• Machine learning industry impact through conference presentations, papers, blog posts, open-source contributions, or patents.

Responsibilities

• Design, build, and deliver machine learning models and components to solve business problems in collaboration with Product and Data Science teams.
• Build and scale multi-tenant platforms for large-scale machine learning model training and serving.
• Apply knowledge of modeling techniques and issues to make ML infrastructure decisions, including model, data, and feature selection, training, tuning, dimensionality, bias and variance, and validation.
• Write and test application code; develop and validate ML models; and automate testing and deployment.
• Collaborate with cross-functional Agile teams to create and enhance software for big data and machine learning applications.
• Retrain, maintain, and monitor production models.
• Build or use cloud-based architectures, technologies, and platforms to deliver optimized ML models at scale.
• Construct optimized data pipelines for machine learning models.
• Apply continuous integration and continuous deployment practices, including test automation and monitoring, to support successful model and application-code deployment.
• Manage code to reduce vulnerabilities, govern models from a risk perspective, and follow Responsible and Explainable AI practices.
• Use programming languages such as Python, Scala, or Java.
• Balance hands-on technical contribution and innovation with mentoring and developing peers and junior engineers.
• Recruit, nurture, and retain engineering talent.
• Stay current with technology trends, experiment with and learn new technologies, participate in technology communities, and mentor members of the engineering community.

Benefits

• Comprehensive health, financial, and other benefits supporting employee 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