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# Principal Software Automation/Test Engineer

- **Company:** Palo Alto Networks
- **Location:** Menlo Park, CA, United States
- **Experience:** Expert
- **Salary:** $156,400.0 - $253,000.0
- **Contract:** Permanent contract
- **Skills:** Testing (Software), Artificial Intelligence, Amazon Web Services, Automation of Tests, Microsoft Azure, Computer Programming, Continuous Integration, Data Structures, Software Debugging, Distributed Systems, Fuzz Testing, Monitoring of Systems, Internet Security, Python (Programming Language), Machine Learning, Regression Testing, OpenAI, Software Safety, Software Engineering, Strategies of Testing, Google Cloud, Large Language Models, Grafana, Prompt Engineering, Model Validation, Pytest, Solid Principles, Information Technology, Api Design, Data Generation
- **Published:** October 30, 2026
- **Apply:** https://www.careerbuilder.com/job-details/principal-software-automation-test-engineer-menlo-park-ca--645ac1be-8127-4b82-8c12-66cdfb88138b

## About the Role

Software Testing, Test Automation, Leading Edge Technology, Internet Security, Artificial Intelligence (AI), Problem Solving Skills, Test Plan/Schedule, Test Design, Model Validation, Benchmarking, * Bachelor's or Master's degree in Computer Science, AI, Machine Learning, or related field

 * 8+ years of experience in software engineering, QA automation, or ML engineering
 * Strong programming skills in Python (preferred) or similar languages
 * Experience with testing frameworks (e.g., PyTest, unittest)
 * Familiarity with machine learning concepts and model evaluation techniques
 * Experience working with APIs, distributed systems, and CI/CD pipelines
 * Knowledge of data structures, algorithms, and software design principles, * Experience with LLMs and prompt engineering
 * Familiarity with evaluation tools like LangChain, OpenAI Evals, or similar frameworks
 * Knowledge of AI safety, bias detection, and adversarial testing
 * Experience with cloud platforms (AWS, GCP, and Azure)
 * Understanding of observability tools and monitoring systems
 * Exposure to synthetic data generation and simulation environments

## Description

* Design and develop testing frameworks for AI/ML models and LLM applications
 * Build automated pipelines for model validation, regression testing, and benchmarking
 * Create evaluation datasets, synthetic data, and test scenarios for edge cases
 * Implement metrics to assess accuracy, robustness, latency, and safety
 * Develop tools for prompt testing, output validation, and hallucination detection
 * Collaborate with engineers, and product teams to define test strategies
 * Monitor model performance in production and build alerting systems
 * Ensure compliance with ethical AI standards, fairness, and bias testing
 * Debug model behavior and identify root causes of failures
 * Analytical thinking and problem-solving
 * Attention to detail and quality-focused mindset
 * Strong collaboration and communication skills
 * Ability to design scalable and reusable testing systems