Senior ML Engineer: Build Scalable Production AI

COMFORT SYSTEMS; Plano (TX); On-site; USD 209,000 - 239,000; Full time

Job summary

Capital One seeks a Machine Learning Engineer 5 to design, build, and scale ML models and data pipelines in a collaborative Agile team. You will develop production-ready ML solutions and ensure governance and responsible AI practices.

You will deploy on cloud platforms (AWS/GCP/Azure), use PyTorch/TensorFlow, and collaborate across product, data science, and operations to deliver big data applications at scale.

Qualifications

• Bachelor's degree in CS, ML or related quantitative field.
• 6+ years coding in Python/Java/Golang/C++ with ML using PyTorch or TensorFlow.
• 6+ years operating large-scale distributed systems (Spark, Ray).
• 4+ years deploying ML solutions in production on AWS/GCP/Azure with Kubernetes.

Responsibilities

• Design, build, and deliver ML models and components with Product and Data Science teams.
• Build and scale multi-tenant platforms for training and serving ML at scale.
• Inform ML infrastructure decisions: model choice, data, features, training, tuning, bias/variance, validation.
• Write and test app code, develop/validate ML models, automate tests and deployment.
• Collaborate in a cross-functional Agile team to enable state-of-the-art big data and ML apps.
• Retrain, maintain, and monitor models in production; ensure governance and responsible AI.

Skills

Python

Java

Golang

C++

Machine Learning

PyTorch

TensorFlow

Spark

Ray

Kubernetes

Education

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

Tools

PyTorch

TensorFlow

Pandas

NumPy

Scikit-learn

Spark

Ray

Kubernetes

Job description

Capital One seeks a Machine Learning Engineer 5 to design, build, and scale ML models and data pipelines in a collaborative Agile team. You will develop production-ready ML solutions and ensure governance and responsible AI practices.

You will deploy on cloud platforms (AWS/GCP/Azure), use PyTorch/TensorFlow, and collaborate across product, data science, and operations to deliver big data applications at scale.