Senior ML Engineer & Tech Lead (IC)

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

Job summary

Capital One is seeking a Machine Learning Engineer 4 (Manager, IC) to design, build, and deploy scalable ML models in a fast-paced environment. You will collaborate with Product, Data Science, and Ops teams to deliver production-grade AI solutions across Cloud platforms and Kubernetes-enabled infrastructure.

The role requires 4+ years in Python and ML frameworks (PyTorch/TensorFlow), plus experience with distributed systems (Spark/Ray) and cloud deployment.

Qualifications

• Bachelor's degree in CS/ML or related quantitative field.
• 4+ years Python experience.
• 4+ years ML experience with PyTorch/TensorFlow.
• 4+ years experience with large-scale distributed systems (Spark/Ray).
• 2+ years deploying ML in production in cloud (AWS/GCP/Azure) and using Kubernetes.

Responsibilities

• Design, build, and deliver ML models and components with Product and Data Science teams.
• Inform ML infrastructure decisions including model choice, data, and feature selection.
• Write and validate production ML code, automate tests and deployment.
• Collaborate in a cross-functional Agile team to enable state-of-the-art ML apps.
• Retrain, monitor, and maintain models in production; build data pipelines for training.
• Leverage CI/CD and monitoring to ensure robust deployments; apply responsible and explainable AI.

Skills

Python

Java

PyTorch

TensorFlow

Kubernetes

Cloud platforms

Education

Bachelor's Degree in Computer Science, Machine Learning or related quantitative field

Tools

Spark

Ray

CI/CD

Job description

Capital One is seeking a Machine Learning Engineer 4 (Manager, IC) to design, build, and deploy scalable ML models in a fast-paced environment. You will collaborate with Product, Data Science, and Ops teams to deliver production-grade AI solutions across Cloud platforms and Kubernetes-enabled infrastructure.

The role requires 4+ years in Python and ML frameworks (PyTorch/TensorFlow), plus experience with distributed systems (Spark/Ray) and cloud deployment.