Senior Staff Software Engineer, Go-To-Market (GTM)
Palo Alto Networks
- Location
- Office - USA - CA - Headquarters
- Work model
- On-Site
- Level
- Staff
- H-1B history
- 168 approvals (FY2023)
- Posted
- 4h ago
Skills
About this role
Our Mission
At Palo Alto Networks®, we’re united by a shared mission—to protect our digital way of life. We thrive at the intersection of innovation and impact, solving real-world problems with cutting-edge technology and bold thinking. Here, everyone has a voice, and every idea counts. If you’re ready to do the most meaningful work of your career alongside people who are just as passionate as you are, you’re in the right place.
Who We Are
In order to be the cybersecurity partner of choice, we must trailblaze the path and shape the future of our industry. This is something our employees work at each day and is defined by our values: Disruption, Collaboration, Execution, Integrity, and Inclusion. We weave AI into the fabric of everything we do and use it to augment the impact every individual can have. If you are passionate about solving real-world problems and ideating beside the best and the brightest, we invite you to join us! We believe collaboration thrives in person. That’s why most of our teams work from the office full time, with flexibility when it’s needed. This model supports real-time problem-solving, stronger relationships, and the kind of precision that drives great outcomes.
Job Summary
Job Summary As a Senior Staff Software Engineer to join our CPQ (Configure Price and Quote) team, you will serve as the recognized subject matter expert, bringing industry best practices to our architectural design. You will act as a technical leader, using first-principles thinking to dissect complex challenges and drive the development of company objectives. This role requires working autonomously to lead cross-functional teams in creating AI Led, Secure, Scalable CPQ Platform that transform our business processes. This is an in office role in our HQ, Santa Clara, CA three days/week Key Responsibilities Architect and design scalable, highly reliable business systems across multiple services and teams Lead the technical design and architecture of AI Led, Secure, Scalable CPQ Platform, owning the process from prototype through deployment Build and integrate foundational and agentic AI/ML workflows, ensuring the secure and effective use of GenAI technologies in our products. Expert at operating in an agile development environment; establish engineering standards and best practices Balance security debt, product stability, and features against a backdrop of go-to-market pressures and timelines Lead deep technical design reviews, perform high-impact code reviews, mentor engineers, and foster a culture of engineering excellence and continuous learning. Establish evaluation and observability frameworks to measure AI quality (accuracy, hallucination rates, query correctness) and system reliability Understanding of prompt engineering best practices and LLM security considerations (input validation, prompt injection prevention) Qualifications Required Qualifications Bachelor's degree with a minimum of 10 years of related experience; or a Master's degree with 8 years of experience; or a PhD with 5 years of experience. Proven ability to design and develop scalable web applications, and integrations using microservices architecture Expert-lever server-side software development experience using Sprint/Spring Boot, Java, Go, and/or other comparable technologies. In-depth knowledge about object-oriented programming, data structures, algorithms, and design patterns. Expert in Java concepts like threading, generics, annotations etc. Knowledge in CI/CD platforms including Kubernetes, Jenkins, Git, Spinnaker, Docker, or related tools. Experience building production applications with LLM APIs (Google Vertex AI /Gemini, OpenAI, Anthropic, or similar) Familiarity with Large Language Models (LLMs) and their practical application in business insights Familiarity with vector embeddings, semantic search, and similarity-based retrieval patterns Understanding of prompt engineering best practices and LLM security considerations (input