Machine Learning Engineer 5 (Senior Manager, IC)

Capital One’s Risk Tech team is partnering with the GRC team and company partners to build and deploy AI-powered, risk management solutions. In this onsite role in Richmond, VA, you’ll help design, build, scale, and operate machine learning models and multi-tenant platforms, with a focus on production monitoring and governed Responsible/Explainable AI.

Salary range: USD 209,000 - 238,500 per year (Richmond, VA). This role is eligible for performance-based incentive compensation, which may include cash bonus(es) and/or long-term incentives (LTI).

What You’ll Do

• Design, build, and deliver ML models and components to solve real-world business problems in collaboration with Product and Data Science.
• Build and scale massive multi-tenant platforms for large-footprint ML training and/or serving.
• Apply ML modeling knowledge to inform infrastructure and approach decisions, including model choice, data and feature selection, training, hyperparameter tuning, dimensionality, bias/variance, and validation.
• Address complex problems through application code, ML model development and validation, and automated tests and deployment.
• Work within a cross-functional Agile team to create and enhance software for state-of-the-art big data and ML applications.
• Retrain, maintain, and monitor ML models in production.
• Use cloud-based architectures and platforms to deliver optimized ML models at scale.
• Construct optimized data pipelines that feed ML models.
• Apply CI/CD best practices, including test automation and monitoring, to support reliable deployment of ML models and application code.
• Ensure code is well-managed to reduce vulnerabilities, maintain risk governance for models, and follow Responsible and Explainable AI best practices.
• Use programming languages such as Python, Scala, or Java.

Requirements

• Bachelor’s degree or higher in Computer Science, Machine Learning, or a related quantitative field (including Statistics, Economics, Operations Research, Analytics, Mathematics, or Engineering).
• At least 6 years programming with Python, Java, Golang, or C++.
• At least 6 years of ML experience using industry standard frameworks PyTorch or Tensorflow and libraries including Pandas, NumPy, Scikit-learn.
• At least 6 years using and operating large-scale distributed systems such as Spark and Ray to prepare AI/ML data.
• At least 4 years deploying and operating ML solutions in production, including operating production services in the cloud (AWS, GCP, Azure) and using Kubernetes to manage large-scale containerized ML systems.

Preferred Qualifications

• Master’s or doctoral degree in computer science, electrical engineering, mathematics, or related field.
• 5+ years optimizing ML algorithms, configurations, and infrastructure.
• 5+ years following software development best practices including source control, testing, code reviews, and CI/CD.
• 5+ years building resilient software with pre-production testing, advanced deployment techniques (one-box, blue/green, gradual dial-up), monitoring and alarms, and preparing incident response plans.
• 5+ years working with ML techniques (Supervised, semi-supervised, and unsupervised, reinforcement learning) and model types (Regression, Classification, Clustering), along with architectures (RNNs, CNNs, LSTMs, Transformers) and evaluation and diagnosis (underfitting, overfitting).
• 5+ years designing, implementing, and scaling production-ready data pipelines for training and evaluating ML models.
• ML industry impact through conference presentations, papers, blog posts, open source contributions, or patents.
• Ability to communicate complex technical and machine learning concepts clearly to a variety of audiences.

Technologies You’ll Work With

• Python, Scala, Java, Golang, C++, PyTorch, Tensorflow
• Pandas, NumPy, Scikit-learn
• Spark, Ray
• AWS, GCP, Azure
• Kubernetes

Benefits

• Eligible for performance-based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI).
• A comprehensive, competitive, and inclusive set of health, financial, and other benefits that support your total well-being.