Senior Machine Learning Engineer: Scale Production Models

capitalone; New York (NY); On-site; USD 215,000 - 246,000; Full time

Job summary

Capital One seeks a Machine Learning Engineer 4 in New York to build and scale production ML models within a collaborative, fast-paced environment. You will collaborate with Product and Data Science teams to deliver real-world AI solutions.

The role emphasizes ML modeling techniques, data and feature selection, and responsible AI practices while leveraging cloud platforms and CI/CD for reliable deployments.

Qualifications

• Bachelor's Degree or higher in Computer Science, Machine Learning or a related quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Engineering)
• At least 4 years of experience programming with Python, Java, Golang, or C++
• At least 4 years of Machine Learning experience using PyTorch or TensorFlow
• At least 4 years of experience using large scale distributed systems (Spark, Ray)
• At least 2 years deploying and operating Machine Learning solutions in production in the cloud (AWS, GCP, Azure) and using Kubernetes

Responsibilities

• Design, build, and deliver ML models and components
• Inform ML infrastructure decisions including model/data/feature selection
• Develop and validate ML models and automate tests and deployment
• Collaborate in an Agile cross-functional team to enable ML apps
• Retrain, maintain, and monitor models in production
• Leverage cloud architectures for scalable ML deployments
• Construct optimized data pipelines for ML models
• Ensure code is well-governed and follows Responsible AI practices
• Use Python, Scala, or Java

Skills

Python

Java

Golang

C++

PyTorch

TensorFlow

Spark

Ray

Kubernetes

AWS

GCP

Azure

Education

Bachelor's Degree or higher in Computer Science or related field

Job description

Capital One seeks a Machine Learning Engineer 4 in New York to build and scale production ML models within a collaborative, fast-paced environment. You will collaborate with Product and Data Science teams to deliver real-world AI solutions.

The role emphasizes ML modeling techniques, data and feature selection, and responsible AI practices while leveraging cloud platforms and CI/CD for reliable deployments.