Senior Staff Machine Learning Engineer
New
Z
ZscalerCybersecurity
Remote - USAFull-TimeStaff
Salary157,500 - 225,000 USD per year
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Job Details
- Experience
- 8+ years of professional Python development
- Required Skills
- AWSPythonSQLNumpyPandasCI/CDLangChain
Requirements
- Build LLM-powered agents in production, including tool and function calling, prompt and context engineering, and multi-step orchestration.
- Use LangGraph, LangChain, or equivalent orchestration frameworks.
- Evaluate non-deterministic output against ground truth.
- Have 8+ years of professional Python development and design and maintain production-quality systems.
- Work with validated inputs, data contracts, comprehensive unit and integration tests, SQL, and Python data analytics libraries such as pandas, Polars, or NumPy.
- Own production reliability, including CI/CD, containerization, structured logging and metrics, observability, alerting, on-call participation, and incident debugging in live distributed systems.
- Have familiarity with AWS cloud environments and infrastructure-as-code.
- Demonstrate Senior Staff-level ownership and leadership of emergent requirements, architecture, and complex engineering projects.
- Preferred: production AI or ML systems experience balancing cost, latency, and accuracy, including model selection, routing, and regression testing.
- Preferred: cyber threat research, threat modeling, threat hunting, detection engineering, data-driven risk analysis, fraud detection, adversary profiling and targeting, actuarial risk, or collaboration with risk analysis teams.
- Preferred: data engineering experience with large-volume event-data pipelines, SQL, OpenSearch or Elasticsearch, Athena or Presto, schema evolution, backfills, and data-quality validation.
Responsibilities
- Translate risk-identification methods into agent logic and ensure quality across risk analysis, explanations, and recommendations.
- Collaborate with threat-research teams to understand data, threats, and security heuristics.
- Identify data requirements for analysis and work with data-engineering teams on pipelines, enrichments, and aggregations.
- Apply backtesting, precision-versus-recall balancing, tuning, and quality control to threat detection.
- Deploy and monitor solutions in production within the CI/CD framework.
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