Senior ML Engineer: Production AI & Risk Tech

Capital One National Association; Cambridge (MA); On-site; USD 197,000 - 225,000; Full time

Job summary

Capital One in Cambridge, MA seeks a Machine Learning Engineer 4 to design, deploy, and maintain production ML models at scale. You will work with Product, Data Science, and Risk Tech teams to build end-to-end AI solutions, leveraging Python, PyTorch, and cloud platforms.

Strong coding, distributed systems, and governance practices are required to ensure robust, responsible AI. The role involves collaborating in an Agile team, retraining models, and optimizing data pipelines for real-world risk

Qualifications

• Bachelor's degree or higher in CS, ML or related quantitative field.
• 4+ years programming with Python, Java, Golang or C++.
• 4+ years ML experience with PyTorch or TensorFlow.
• 4+ years operating large-scale distributed systems (Spark, Ray).
• 2+ years deploying ML in production on cloud platforms (AWS/GCP/Azure) with Kubernetes.

Responsibilities

• Design, build, and deliver ML models and components with Product and Data Science teams.
• Inform infrastructure choices, data, features, training, and validation.
• Develop and test application code, automate tests and deployment of models.
• Collaborate in an Agile cross-functional team for big data and ML apps.
• Retrain, maintain, and monitor models in production.
• Leverage cloud architectures to scale ML models; build data pipelines.
• Apply CI/CD, test automation, and monitoring for reliable deployment.
• Ensure governance and Responsible AI practices in models.

Skills

Python

Java

Golang

C++

PyTorch

TensorFlow

Pandas

NumPy

Scikit-learn

Spark

Ray

Kubernetes

AWS

GCP

Azure

Education

Bachelor's Degree in Computer Science or related quantitative field

Tools

PyTorch

TensorFlow

Spark

Ray

Kubernetes

AWS

GCP

Azure

Job description

Capital One in Cambridge, MA seeks a Machine Learning Engineer 4 to design, deploy, and maintain production ML models at scale. You will work with Product, Data Science, and Risk Tech teams to build end-to-end AI solutions, leveraging Python, PyTorch, and cloud platforms.

Strong coding, distributed systems, and governance practices are required to ensure robust, responsible AI. The role involves collaborating in an Agile team, retraining models, and optimizing data pipelines for real-world risk