Remote Senior Staff ML Engineer for AI Security Platform

Gohyred; United States; Remote; USD 158,000 - 225,000; Full time

Benefits offered by this job

Health plans

Time off

Parental leave

Retirement options

Education reimbursement

In-office perks

Job summary

Zscaler is seeking a Senior Staff Machine Learning Engineer for a remote (USA) role reporting to the Manager AI Platform and Data Science. You will build high-fidelity risk identification solutions using data analysis, threat research, and AI/ML techniques to automate security analysis.

You will collaborate with threat researchers, design data pipelines, and contribute to production-ready components with a focus on precision, recall, and explainability.

Qualifications

• Experience building LLM-powered agents in production with tool and function calling, prompt and context engineering.
• Evaluation of non-deterministic output against ground truth.
• 8+ years of Python development with production-quality systems and data analytics (pandas/NumPy).

Responsibilities

• Translate risk-identification methods into agent logic with quality across analysis, explanations, and recommendations.
• Collaborate with threat-research to understand data and heuristics.
• Identify data requirements and partner with data engineering for pipelines and enrichments.
• Follow a data-driven quality approach to threat detection including backtesting and tuning.
• Deploy and monitor solutions in production within CI/CD framework.

Skills

LLM-powered agents

Python development

SQL

CI/CD

Tools

LangGraph

LangChain

OpenSearch/Elasticsearch

Job description

Zscaler is seeking a Senior Staff Machine Learning Engineer for a remote (USA) role reporting to the Manager AI Platform and Data Science. You will build high-fidelity risk identification solutions using data analysis, threat research, and AI/ML techniques to automate security analysis.

You will collaborate with threat researchers, design data pipelines, and contribute to production-ready components with a focus on precision, recall, and explainability.