Machine Learning Engineer (AI Foundations)

Capital One is advancing its AI Foundations efforts to bring sophisticated machine learning applications from concept to production at scale. This onsite role in New York, NY offers a chance to shape ML infrastructure, architectural design, and governance while collaborating across product, data science, and engineering teams. The position supports work on large language models and autonomous agentic systems, with compensation in the range of USD 148,000 to 168,900 per year.

Responsibilities

• Design, build, and deliver ML models and components that address real business needs, partnering with Product and Data Science teams.
• Guide ML infrastructure choices based on modeling techniques and issues, including model selection, data and feature choices, training, hyperparameters, dimensionality, bias/variance, and validation.
• Tackle complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment processes.
• 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 models in production environments.
• Utilize or build cloud-based architectures and platforms to deliver optimized ML models at scale.
• Construct efficient data pipelines to feed ML models.
• Apply CI/CD best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code.
• Ensure code quality, governance, and risk awareness, aligning ML practices with Responsible and Explainable AI principles.
• Program in Python, Scala, or Java.

Requirements

• Bachelor’s Degree.
• Minimum 2 years of experience designing and building data-intensive solutions using distributed computing (internship experience does not apply).
• Minimum 2 years of programming experience with Python, Scala, or Java.
• At least 1 year of machine learning experience with an industry-recognized ML framework (scikit-learn, PyTorch, Dask, Spark, or TensorFlow).

Technologies

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

Preferred Qualifications

• Experience developing and deploying ML solutions in public clouds such as AWS, Azure, or Google Cloud Platform.
• 1+ years of experience working with large code bases in a team environment.
• 1+ years of experience with distributed file systems or multi-node database paradigms.
• Contributed to open source ML software.
• 1+ years of experience building production-ready data pipelines that feed ML models.
• Experience leveraging interactive AI tooling to accelerate productivity beyond basic code completion.