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Salary: USD 115,500 - 165,000 / annual
Zscaler is seeking a Senior Machine Learning Engineer to join the AI Platform and Data Science team, reporting to the Manager of AI Platform and Data Science. The team's mission is high-fidelity risk identification in customer data, aiming to catch all threats while minimizing false positives.
In this role, you will translate risk identification methods into agent logic, understanding the benefits and limitations of AI agents and ensuring quality across risk analysis, explanations, and recommendations. You'll collaborate closely with threat research teams to understand data, threats, and security heuristics. You will identify and solve data requirements for analysis, working with data engineering teams to develop pipelines, enrichments, and aggregations. A core responsibility is following a data-driven quality approach to threat detection, including backtesting, balancing precision versus recall, tuning models, and implementing quality control measures. You'll deploy and monitor solutions in production using the company's CI/CD framework.
Zscaler is a NASDAQ-listed company (ZS) that accelerates digital transformation through its Zero Trust Exchange platform, protecting thousands of customers from cyberattacks and data loss. The platform is distributed across 160+ public exchanges globally and thousands of private exchanges at the edge, making it the world's largest in-line cloud security platform. The company is building an AI-native enterprise where human potential is amplified by machine intelligence to solve complex security challenges.
You should thrive in ambiguity and be comfortable building solutions in unstructured, dynamic environments. You act as an owner with deep passion for the mission, navigating between high-level strategy and hands-on execution. You possess a growth mindset, continuously developing skills and seeking feedback. You approach complex technical challenges with constructive energy and are relentlessly data-driven, leveraging analytics and empirical evidence over assumptions.
**Requirements:**
- Experience building LLM-powered agents in production: tool and function calling, prompt and context engineering, multi-step orchestration frameworks (LangGraph, LangChain, or equivalent), and evaluation of non-deterministic output against ground truth
- 3+ years of professional Python development with demonstrated ability to design and maintain production-quality systems with validated inputs, data contracts, and comprehensive unit and integration tests; hands-on expertise with SQL and Python data analytics libraries (pandas, Polars, NumPy)
- Demonstrated ownership of production reliability: CI/CD, containerization, structured logging and metrics, observability and alerting, on-call participation, and incident debugging in live distributed systems; familiarity with cloud and infrastructure-as-code in AWS environments
- Senior-level ownership and leadership of projects, development, and testing
**Preferred Qualifications:**
- Experience with production AI or ML systems where cost, latency, and accuracy are competing constraints, including model selection, routing, and regression testing
- Background in cyber threat research, threat modeling, threat hunting, detection engineering, or data-driven risk analysis, fraud detection, adversary profiling, actuarial risk, or close collaboration with these teams
- Data engineering experience building and operating pipelines over large-volume event data: SQL, columnar or search-backed stores (OpenSearch/Elasticsearch, Athena/Presto, or similar), plus schema evolution, backfills, and data quality validation
