Senior ML Engineer — AI in Production at Scale

Capital One; Richmond (VA); On-site; USD 179,000 - 205,000; Full time

Benefits offered by this job

Performance-based incentives

Health benefits

Job summary

Capital One is seeking a Machine Learning Engineer to drive AI transformations and scale production models. You will work across Product, Data Science, and Risk Tech, building ML components and pipelines that solve real business problems.

You’ll deploy models in production, monitor and retrain them, and ensure governance and Responsible AI practices while leveraging Python, Java, and cloud platforms in a highly collaborative Agile environment.

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 using PyTorch or TensorFlow and Pandas/NumPy/Scikit-learn.
• 4+ years using large-scale distributed systems (Spark, Ray).
• 2+ years deploying ML in production, cloud use (AWS/GCP/Azure) and Kubernetes.

Responsibilities

• Design, build, and deliver ML models and components with Product and Data Science teams.
• Inform ML infrastructure decisions: model choice, data, features, training, tuning, validation.
• Write, test, and deploy code; automate tests and deployment of ML models.
• Collaborate in an Agile cross-functional team to enable big data and ML apps.
• Retrain, maintain, and monitor models in production; ensure governance and risk compliance.
• Leverage cloud architectures to scale ML models; build data pipelines for training.

Skills

Python

Java

Golang

C++

PyTorch

TensorFlow

Pandas

NumPy

Scikit-learn

Spark

Ray

Kubernetes

AWS

GCP

Azure

CI/CD

Distributed systems

Education

Bachelor's Degree in Computer Science or related field

Master's or PhD in computer science / related field

Tools

Spark

Ray

Kubernetes

AWS

GCP

Azure

CI/CD tooling

Job description

Capital One is seeking a Machine Learning Engineer to drive AI transformations and scale production models. You will work across Product, Data Science, and Risk Tech, building ML components and pipelines that solve real business problems.

You’ll deploy models in production, monitor and retrain them, and ensure governance and Responsible AI practices while leveraging Python, Java, and cloud platforms in a highly collaborative Agile environment.