Senior ML Platform Engineer - IC Leader

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

Job summary

Capital One seeks a Machine Learning Engineer 4 to lead ML model development, deployment, and scaling within a high-velocity Enterprise ML Platform. You’ll work on multi-tenant training workflows, Kubeflow pipelines, and cloud-based architectures to deliver robust AI capabilities across regulated workloads.

The role emphasizes collaboration with Product and Data Science teams, production-grade pipelines, and governance around responsible AI, using Python/Java/Golang and modern ML frameworks.

Qualifications

• Bachelor's degree or higher in Computer Science, Machine Learning or related quantitative field.
• At least 4 years of experience programming with Python, Java, Golang, or C++.
• At least 4 years of ML experience using PyTorch or TensorFlow and libraries (Pandas, NumPy, Scikit-learn).
• At least 4 years of experience with large-scale distributed systems (Spark, Ray).
• At least 2 years deploying ML solutions in production and cloud environments (AWS, GCP, Azure) with Kubernetes.

Responsibilities

• Design, build, and deliver ML models and components solving real-world problems.
• Inform infrastructure decisions with ML modeling techniques including data, features, training, and validation.
• Retrain, maintain, and monitor models in production.
• Collaborate in an Agile cross-functional team to enable state-of-the-art ML applications.
• Ensure governance, risk compliance, and responsible AI practices.

Skills

Python

Java

Golang

C++

Education

Bachelor's degree in Computer Science or related field

Master's or PhD in CS/EE/Math or related

Tools

Kubeflow Pipelines

Kubeflow

Spark

Ray

Dask

Amazon EKS

Go SDKs

Python

Kubernetes

Docker

Job description

Capital One seeks a Machine Learning Engineer 4 to lead ML model development, deployment, and scaling within a high-velocity Enterprise ML Platform. You’ll work on multi-tenant training workflows, Kubeflow pipelines, and cloud-based architectures to deliver robust AI capabilities across regulated workloads.

The role emphasizes collaboration with Product and Data Science teams, production-grade pipelines, and governance around responsible AI, using Python/Java/Golang and modern ML frameworks.