ML Infrastructure Engineer - Build Scalable Model Platforms

Francisco Partners; Mountain View (CA); On-site; USD 125,000 - 175,000; Full time

Job summary

NewsBreak in California is hiring a Machine Learning Infrastructure Engineer to build the backbone that trains, serves, and monitors models behind our Ads and Recommendations products. You’ll join a small, high-ownership team that ships platform improvements end-to-end—partnering with product and data teams, reducing latency and cost, and shortening the path from an idea to a safely launched model.

You’ll work across the ML lifecycle: making training faster and more reliable, improving model

Qualifications

• Bachelor's degree in Computer Science, Engineering, or related; 2+ years of experience or Master’s/PhD in related field.
• Proficient in Python and strong understanding of OO languages (C++/Java).
• Familiarity with software development processes including version control, bug tracking, and design documentation.
• Basic knowledge of applied ML and experience with PyTorch and TensorFlow.

Responsibilities

• Design and develop machine learning infrastructure.
• Own and enhance core components for offline/online model training, model pipeline health monitoring, serving, and feature authoring.
• Proactively address ML infrastructure issues impacting production.
• Collaborate with ML engineers to build robust model pipelines using ML infrastructure.

Skills

Python

OO languages (C++/Java)

Applied ML basics

ML frameworks (PyTorch, TensorFlow)

Education

Bachelor’s degree in CS/Engineering

Master’s/PhD in related discipline

Tools

Git

Bug tracking

Design documentation

AWS/GCP/Azure

Job description

NewsBreak in California is hiring a Machine Learning Infrastructure Engineer to build the backbone that trains, serves, and monitors models behind our Ads and Recommendations products. You’ll join a small, high-ownership team that ships platform improvements end-to-end—partnering with product and data teams, reducing latency and cost, and shortening the path from an idea to a safely launched model.

You’ll work across the ML lifecycle: making training faster and more reliable, improving model