Machine Learning Engineer

Capital One is a leading diversified financial services company specializing in credit cards, auto loans, banking, and savings products. Known for its innovative approach and commitment to customer-centric solutions, Capital One leverages advanced technology and data-driven insights to deliver personalized financial services. The company fosters a dynamic and inclusive work environment, attracting top talent dedicated to transforming the future of banking and finance.

About the role

The Lead Machine Learning Engineer (MLE) at Capital One will be a key member of an Agile team responsible for productionizing machine learning applications at scale. This role involves designing, developing, and deploying sophisticated machine learning systems that address complex business problems. The ideal candidate will have extensive experience in building scalable ML solutions, working with cross-functional teams, and leveraging cloud-based architectures. The Lead MLE will drive the technical direction of ML initiatives, ensure high availability and performance of models, and uphold best practices in responsible AI. This position offers an opportunity to work on cutting-edge technology, influence strategic decisions, and lead teams in delivering impactful ML solutions across the organization.

Qualifications

• Bachelor's Degree in Computer Science, Electrical Engineering, Mathematics, or a related field
• Minimum of 6 years of experience designing and building data-intensive solutions using distributed computing frameworks
• At least 4 years of programming experience with Python, Scala, or Java
• Minimum of 2 years of experience developing, scaling, and optimizing machine learning systems
• Experience with industry-recognized ML frameworks such as scikit-learn, PyTorch, Dask, Spark, or TensorFlow
• Proficiency in developing resilient and maintainable code
• Strong understanding of data gathering, preparation, and feature engineering for ML models
• Experience leading or mentoring teams in ML development projects
• Hands-on experience deploying ML solutions in cloud environments like AWS, Azure, or Google Cloud Platform
• Knowledge of building and scaling complex data pipelines and evaluating model performance
• Ability to communicate complex technical concepts effectively to stakeholders

Responsibilities

• Design, develop, and deliver machine learning models and components that solve real-world business problems in collaboration with Product and Data Science teams
• Make informed decisions regarding ML infrastructure, including model choice, data selection, feature engineering, and hyperparameter tuning
• Write, test, and validate application code to automate model training, testing, and deployment processes
• Collaborate within cross-functional Agile teams to create and enhance big data and ML applications
• Monitor, retrain, and maintain models in production to ensure ongoing performance and relevance
• Leverage cloud-based architectures and technologies to deploy ML models at scale efficiently
• Construct and optimize data pipelines to support ML models and ensure data quality and reliability
• Implement CI/CD practices, including automation, testing, and monitoring, to facilitate continuous deployment of ML solutions
• Manage code securely, ensuring models adhere to governance, risk, and compliance standards, including responsible and explainable AI practices
• Utilize programming languages such as Python, Scala, or Java to develop scalable ML applications

Benefits

• Competitive salary aligned with experience and location
• Performance-based incentives, including cash bonuses and long-term incentives
• Comprehensive health, dental, and vision insurance plans
• Retirement savings plans with company matching
• Generous paid time off and holiday leave
• Opportunities for professional development and continuous learning
• Inclusive and diverse workplace culture that promotes work-life balance
• Access to cutting-edge technology and innovative projects