Senior ML Engineer: Build Production-Scale Models

Capital One; Plano (TX); On-site; USD 147,000 - 168,000; Full time

Benefits offered by this job

Performance-based incentives

Job summary

Capital One is seeking a Machine Learning Engineer in Plano, TX to join an Agile team and productionize ML applications at scale. You will design, code, and deploy ML models, collaborating with Product and Data Science teams to solve real-world business problems.

You will work with Python, Scala, or Java, leverage ML frameworks, build data pipelines, and apply CI/CD practices to deliver reliable, scalable ML solutions in a cloud-enabled environment.

Qualifications

• Bachelor's degree required.
• 4+ years programming in Python, Scala, or Java.
• 3+ years building data-intensive solutions with distributed computing.
• 2+ years with ML frameworks (scikit-learn, PyTorch, Dask, Spark, or TensorFlow).
• 1+ year productionizing, monitoring, and maintaining models.

Responsibilities

• Design, build, and deliver ML models and components with Product and Data Science teams.
• Inform ML infrastructure decisions incl. model choice, data, and feature selection.
• Write and test application code, develop and validate ML models, automate tests and deployment.
• Collaborate in an Agile team to enable scalable ML apps and data pipelines.
• Retrain, maintain, and monitor models in production.
• Leverage cloud architectures to deliver optimized ML models at scale.

Skills

Python

Scala

Java

Distributed computing

ML modeling

CI/CD

Python ML frameworks

Education

Bachelor's Degree

Master's or PhD in CS/EE/Math

Tools

scikit-learn

PyTorch

Dask

Spark

TensorFlow

AWS

Azure

GCP

Job description

Capital One is seeking a Machine Learning Engineer in Plano, TX to join an Agile team and productionize ML applications at scale. You will design, code, and deploy ML models, collaborating with Product and Data Science teams to solve real-world business problems.

You will work with Python, Scala, or Java, leverage ML frameworks, build data pipelines, and apply CI/CD practices to deliver reliable, scalable ML solutions in a cloud-enabled environment.