Senior Staff Machine Learning Engineer

About this role

Zscaler (NASDAQ: ZS) accelerates digital transformation so customers can be more agile, efficient, resilient, and secure. The Zscaler Zero Trust Exchange platform protects thousands of customers from cyberattacks and data loss by securely connecting users, devices, and applications in any location. Distributed across 200+ public data centers globally and thousands of private sites at the edge, the SASE-based Zero Trust Exchange is the world’s largest in-line cloud security platform.

We believe the future of work is Human + AI and are building an AI-native enterprise where human potential is amplified by machine intelligence to solve the world’s hardest security challenges. Driven by deep customer obsession, we are committed to the mission, outcome, and to each other. We bring these commitments to life through three core behaviors: ownership and collaboration, trust through outcomes and impact, and a challenge culture with ongoing feedback.

Ready to make an impact at the company pioneering security transformation in the AI era? Join us at Zscaler. Role We are looking for a Senior Staff Machine Learning Engineer to join our team. This is a remote (USA) role, reporting to the Manager AI Platform and Data Science in the AI Platform and Data Science department. This team’s mission is high-fidelity risk identification in customer data, with goals to catch all threats while minimizing noise.

To achieve this, you will develop solutions applying data analysis and threat-research while leveraging AI, machine learning, and data engineering to build quality, data-driven components to automate security analysis. What you’ll do (Role Expectations) Translate risk identification methods into agent logic, understanding the benefits and limitations of agents and ensuring quality across risk analysis, explanations, and recommendations Collaborate closely with threat-research to understand data, threats, and their approach to developing security heuristics Identify and solve data requirements for analysis, developing and collaborating with data engineering teams for pipelines, enrichments, and aggregations Follow a data-driven quality approach to threat detection, including backtesting, balancing precision vs recall, tuning, and quality control Deploy and monitor your solutions in production within our CI/CD framework Who You Are (Success Profile) You thrive in ambiguity and build the path as you walk it, seeing unstructured challenges as the raw material to construct meaningful systems.