Machine Learning Engineer 5 (Senior Manager, IC)

Capital One is seeking a Machine Learning Engineer 5 (Senior Manager, IC) in Risk Tech to partner with the GRC team and company partners. The role focuses on designing, building, scaling, and operating AI-powered risk management solutions, with emphasis on production monitoring and governed Responsible/Explainable AI.

Responsibilities

• Design, build, and/or deliver machine learning models and components that address real-world business problems in collaboration with Product and Data Science teams
• Build and scale multi-tenant platforms that support large-scale model training and/or serving
• Apply ML modeling knowledge to influence ML infrastructure decisions, including model choice, data and feature selection, training, hyperparameter tuning, dimensionality, bias/variance considerations, and validation
• Solve complex challenges through application code development and testing, ML model development and validation, and automation of tests and deployment
• Work within a cross-functional Agile team to create and improve software for big data and ML applications
• Retrain, maintain, and monitor models in production
• Use and/or build cloud-based architectures, technologies, and platforms to deliver optimized ML models at scale
• Construct optimized data pipelines to support ML model training and evaluation
• Apply continuous integration and continuous deployment practices, including test automation and monitoring, to support successful releases of ML models and application code
• Manage code to reduce vulnerabilities; ensure models are well-governed from a risk perspective and aligned to Responsible and Explainable AI best practices
• Use programming languages such as Python, Scala, or Java

Required Qualifications

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

Preferred Qualifications

• Master’s or doctoral degree in computer science, electrical engineering, mathematics, or a related field
• 5+ years of experience optimizing ML algorithms, configurations, and infrastructure
• 5+ years of experience following software development best practices, including source control, testing, and code reviews, plus CI/CD
• 5+ 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 plan preparation
• 5+ years of experience with ML techniques (supervised, semi-supervised, and unsupervised, reinforcement learning) and model types (regression, classification, clustering), including architectures (RNNs, CNNs, LSTMs, Transformers), training concepts (loss function, hyperparameters, regularization), and evaluating accuracy while diagnosing issues such as underfitting and overfitting
• 5+ years of experience 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 ML concepts to a variety of audiences

Technologies

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

Location

Richmond, VA (onsite)

Compensation

USD 209,000 - 238,500 per year (Richmond, VA)

Benefits

• Eligible for performance-based incentive compensation, which may include cash bonuses and/or long-term incentives (LTI)
• Comprehensive, competitive, and inclusive set of health, financial, and other benefits supporting total well-being