Palo Alto Networks
Principal Machine Learning Engineer
$163K–$264K
Office - USA - CA - Headquarters · No · Principal · Full-time · Posted Oct 1, 2026
- Experience
- 7+ years of software engineering, machine learning engineering, or related industry experience.
- Education
- Bachelor’s degree in Computer Science, Machine Learning, Engineering, or a related technical field, or equivalent practical experience.
- Visa sponsorship
- Immigration sponsorship: Yes
About the job
Provides technical leadership and builds production AI systems for AI Canvas, enabling cybersecurity teams to explore and act on complex security data using natural language, agents, and intelligent workflows.
What you'll do
- Architect and develop AI Canvas’s AI capabilities, from design through production deployment and ongoing improvement.
- Build and improve LLM, RAG, agent, natural-language-to-query, evaluation, and guardrail systems for security-data exploration.
- Mentor engineers and lead technical initiatives while establishing engineering practices for reliable, observable AI systems.
What they ask for
- 7+ years of software engineering, machine learning engineering, or related industry experience, including significant production-systems experience.
- Strong experience building machine-learning or AI-powered applications at scale and hands-on experience with LLMs and generative AI.
- Strong proficiency in Python and experience developing production-grade backend or ML services.
- Experience designing AI/ML evaluation systems and building scalable systems on public cloud platforms such as GCP, AWS, or Azure.
Nice to have
- Master’s or PhD in a related technical field.
- Experience with production LLM products or agentic AI platforms, natural-language-to-query systems, evaluation tooling, vector databases, Docker, or Kubernetes.
Pay
$163,200.00 - $264,000.00/yr (base salary, USD)
Tools
Python, GCP, AWS, Azure, Docker, Kubernetes
AI focus
Builds and leads production AI for cybersecurity data exploration, including LLMs, RAG, agents, natural-language-to-query, evaluation systems, and guardrails.
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