Staff ML Engineer: Scale Production AI Systems

Capital One National Association; McLean (VA); On-site; USD 269,000 - 307,000; Full time

Job summary

Capital One is seeking a Staff Machine Learning Engineer to develop and scale production ML models in a fast-paced, collaborative environment. You will partner with Product, Architecture, Engineering, and Data Science teams to deliver robust AI solutions in the financial services domain.

The role emphasizes building scalable ML systems, leveraging cloud architectures, and leading large-scale initiatives while mentoring engineers and contributing to best practices.

Qualifications

• Bachelor's degree or higher in Computer Science, ML or related quantitative field.
• 8+ years programming with Python, Java, Golang, or C++.
• 6+ years ML experience with PyTorch or TensorFlow and Pandas/NumPy/Scikit-learn.
• 6+ years operating large-scale distributed systems (Spark, Ray).
• 5+ years deploying ML solutions in production in cloud and using Kubernetes.

Responsibilities

• Deliver ML models and software components in collaboration with Product, Architecture, Engineering, and Data Science teams.
• Drive the creation and evolution of ML models for state-of-the-art intelligent systems.
• Lead large-scale ML initiatives with a customer-focused approach.
• Leverage cloud architectures to deliver optimized ML models at scale.
• Optimize data pipelines to feed ML models.
• Use Python, Java, Golang, and GoLang; leverage Dask and RAPIDS.
• Evangelize best practices across engineering and modeling lifecycles.
• Help recruit and nurture top engineering talent.

Skills

Python

Java

Golang

Distributed systems

Cloud computing

Communication

Education

Bachelor's degree in Computer Science or related field

Master's or Doctoral degree (preferred)

Tools

PyTorch

TensorFlow

Spark

Ray

Kubernetes

Dask

RAPIDS

Job description

Capital One is seeking a Staff Machine Learning Engineer to develop and scale production ML models in a fast-paced, collaborative environment. You will partner with Product, Architecture, Engineering, and Data Science teams to deliver robust AI solutions in the financial services domain.

The role emphasizes building scalable ML systems, leveraging cloud architectures, and leading large-scale initiatives while mentoring engineers and contributing to best practices.