Staff Machine Learning Engineer at Zscaler

Zscaler is a leading cloud security company that operates the world’s largest security cloud, the Zscaler Zero Trust Exchange™ platform. They serve thousands of enterprise customers, including 45% of the Fortune 500.

They are seeking an experienced Staff Machine Learning Engineer to join their Shared Platform Services team. In this role, you will be responsible for architecting, building, and maintaining robust, large-scale distributed systems to support the entire client-side machine learning pipeline. This includes everything from data collection and feature engineering to model deployment and real-time serving.

Responsibilities
Architect, build, and maintain large-scale distributed systems for the entire client-side ML pipeline.

Develop functional specifications, assess task requirements, and actively participate in the development and support of client-side ML solutions.

Solve complex, real-world business problems by working closely with data scientists, product management, and product engineering teams.

Develop, implement, and optimize C/C++ code for the efficient execution of machine learning models on the client or server side.

Integrate and run various machine learning models on client platforms, ensuring optimal performance and resource utilization.

Requirements
Minimum Qualifications:

8+ years of experience as a Software Engineer, including 3+ years as an ML platform engineer with a focus on client-side development.

Experience in C/C++ programming for high-performance applications.

A good understanding of the Windows OS and experience building applications on Windows using Visual Studio.

Strong experience with building out data collection/processing infrastructure, ML model training, and serving platforms on client devices.

Experience with SLM (Small Language Models) and running different ML models in client-side environments.

Preferred Qualifications:

Strong experience with LLMs, agent architectures, and prompt engineering.

Experience with ONNX runtime for optimizing and deploying machine learning models.

An advanced degree or certifications in Machine Learning.