Machine Learning Engineer (AI Foundations)

Capital One is seeking a Machine Learning Engineer for AI Foundations to scale ML applications and systems, participating in the design, development, and deployment of ML solutions across platforms. The role emphasizes ML architecture, responsible AI practices, and collaboration on advanced LLMs and autonomous agentic systems within the AI Foundations team. This onsite position is based in New York, NY, with a salary range of USD 148,000 to 168,900 per year.

Responsibilities

• Design, build, and deliver ML models and components to solve real world business problems, in collaboration with Product and Data Science teams.
• Inform ML infrastructure decisions using knowledge of modeling techniques and issues, including model choice, data and feature selection, training, hyperparameters, dimensionality, bias/variance, and validation.
• Address complex challenges by writing and testing application code, developing and validating ML models, and automating tests and deployment.
• Collaborate within a cross functional Agile team to create and enhance software that enables state of the art big data and ML applications.
• Retrain, maintain, and monitor models in production.
• Leverage or build cloud based architectures, technologies, and platforms to deliver optimized ML models at scale.
• Construct optimized data pipelines to feed ML models.
• Apply continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code.
• Ensure code quality and governance to minimize vulnerabilities, maintain risk governance for models, and follow Responsible and Explainable AI practices.
• Utilize programming languages such as Python, Scala, or Java.

Requirements

• Bachelor’s Degree.
• At least 2 years of experience designing and building data‑intensive solutions using distributed computing (Internship experience does not apply).
• At least 2 years of experience programming 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 a public cloud 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, utilizing capabilities beyond basic code completion.