Senior Machine Learning Engineer (AI Foundations)

What you get

Join Capital One on the AI Foundations team, where the focus is on productionizing machine learning at scale, crafting resilient architectures, and delivering high-performance ML systems. This onsite role in New York, NY combines a competitive salary with comprehensive health and financial benefits, plus incentives tied to performance. You’ll collaborate across product and data science teams, contribute to responsible and explainable AI practices, and grow your expertise through end-to-end ML engineering work.

• Salary: USD 176,500 - 201,400 per year
• Location: New York, NY — onsite
• Benefits: Health benefits
• Financial benefits: Financial benefits
• Incentives: Performance-based incentive compensation (cash bonuses and/or long-term incentives)
• Education: Bachelor’s Degree

What you'll do

• Develop and deploy ML models and components that address real world business needs, partnering with Product and Data Science teams
• Guide ML infrastructure decisions using knowledge of modeling techniques, data choices, feature selection, training, hyperparameters, bias/variance, and validation
• Tackle complex problems by writing and testing application code, building and validating ML models, and automating tests and deployment
• Work within a cross functional Agile team to create software that powers advanced big data and ML applications
• Retrain, maintain, and monitor models in production environments
• Utilize or build cloud based architectures and platforms to deliver optimized ML models at scale
• Design efficient data pipelines to feed ML models
• Apply CI/CD best practices, including test automation and monitoring, to ensure reliable deployment of ML models and code
• Maintain secure, well-governed code and follow responsible and explainable AI practices
• Proficiency in Python, Scala, or Java

Basic qualifications

• Bachelor’s Degree
• At least 4 years of professional experience coding with Python, Scala, or Java (internships excluded)
• At least 3 years of designing and building data intensive solutions using distributed computing
• At least 2 years of hands-on experience with ML frameworks (scikit-learn, PyTorch, Dask, Spark, or TensorFlow)
• At least 1 year of experience productionizing, monitoring, and maintaining models

Preferred qualifications

• 1+ years of experience building, scaling, and optimizing ML systems
• 1+ years of experience gathering and preparing data for ML models
• 2+ years of delivering performant, resilient, and maintainable code
• Experience developing and deploying ML solutions in AWS, Azure, or Google Cloud Platform
• Master's or doctoral degree in computer science, electrical engineering, mathematics, or a related field
• 3+ years of experience with distributed file systems or multi-node database paradigms
• Contributed to open source ML software
• Authored or co-authored a paper on a ML technique, model, or concept
• 3+ years building production-ready data pipelines that feed ML models
• Experience designing, implementing, and scaling complex data pipelines for ML models and evaluating their performance
• Experience using interactive AI tooling to accelerate productivity beyond basic code completion

Technologies

• Python, Scala, Java
• scikit-learn, PyTorch, Dask, Spark, TensorFlow
• AWS, Azure, Google Cloud Platform