Machine Learning Engineer (AI Foundations)

Capital One is seeking a Machine Learning Engineer focused on AI Foundations to scale ML applications from design to deployment, working across platforms to productionize data driven solutions. The role emphasizes ML architecture, responsible AI practices, and collaboration on developing advanced LLMs and autonomous agentic capabilities within the AI Foundations team. This position is based in New York, NY on site.

Responsibilities

• Design, develop, and deliver machine learning models and components that address tangible business needs, collaborating with the Product and Data Science teams.
• Inform ML infrastructure decisions by applying knowledge of modeling techniques and issues, including model selection, data and feature choices, training processes, hyperparameter tuning, dimensionality, bias and variance, and validation strategies.
• Tackle complex problems by authoring and testing application code, building and validating ML models, and automating testing and deployment workflows.
• Work within a cross-functional Agile team to build and improve software enabling cutting-edge big data and ML applications.
• Retrain, maintain, and monitor models in production.
• Leverage or construct cloud-based architectures, technologies, and platforms to deliver optimized ML models at scale.
• Design efficient data pipelines to feed ML models.
• Apply continuous integration and continuous deployment practices, including automated testing and monitoring, to ensure reliable deployment of ML models and application code.
• Maintain secure, well-governed code and models, adhering to Responsible and Explainable AI practices.
• Proficiency with programming languages such as Python, Scala, or Java.

Requirements

• Bachelor’s Degree required.
• At least 2 years of experience designing and building data-intensive solutions on distributed computing platforms; internship experience does not apply.
• At least 2 years of programming experience in Python, Scala, or Java.
• At least 1 year of ML experience using an industry recognized 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 on public cloud platforms such as AWS, Azure, or Google Cloud Platform.
• 1+ year of experience contributing to large codebases within a team environment.
• 1+ year of experience with distributed file systems or multi-node database architectures.
• Contributions to open source ML software.
• 1+ year building production-grade data pipelines feeding ML models.
• Experience using interactive AI tooling to accelerate productivity beyond basic code completion.