Sr Principal AI-Data Scientist; ADEM
Listed on 2026-06-19
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IT/Tech
AI Engineer (Applied/Software), Machine Learning/ ML Engineer
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.
WhoWe 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 SummaryThe ADEM engineering team is the engine of innovation at Palo Alto Networks. We don't just follow industry trends; we create them. As a Sr Principal Engineer focused on the Agent, you will be at the center of this mission, tackling "impossible" problems in scale, security, and performance at the most critical point: the user’s endpoint. We look for leaders who are comfortable in deep ambiguity, possess an insatiable curiosity, and are driven to build technology that protects the digital way of life for the world’s largest enterprises.
YourCareer:
We are seeking a visionary Senior Principal AI / Data Scientist to join our Autonomous Digital Experience Management (ADEM) team. In this role, you will be at the forefront of transforming ADEM from a reactive data visualization tool into a proactive AIOps platform. You will act as an architectural and product‑strategy leader, leveraging Machine Learning and Generative AI to solve complex customer networking problems, drive zero‑touch troubleshooting, and shape our future AI roadmap.
If you thrive on "Disruption"—challenging entrenched beliefs to solve real problems with breakthrough AI‑driven ideas—and want to see your work showcased globally while generating patents, this is the role for you.
Key Responsibilities AI/ML Architecture & Agentic Workflows- Drive Gen AI Innovation:
Design, architect, and implement production‑ready "Agentic" Root Cause Analysis (RCA) systems to automate complex network troubleshooting, reducing IT admin diagnosis time from 30+ minutes to under a minute. - End‑to‑End ML Development:
Own the full ML lifecycle from Exploratory Data Analysis (EDA) and rapid Proof‑of‑Concepts (PoCs) to deploying scalable ML baselines and intelligent alerting systems for diverse network segments. - System Design & Microservices:
Develop high‑performance, independent AI‑powered microservices (e.g., domain‑based application classification) from initial PM requirements to fully scalable production services.
- Data Pipeline Mastery:
Build, optimize, and maintain complex data pipelines utilizing Google Cloud Dataflow and Big Query. - Query Optimization:
Audit and optimize heavy Big Query queries to eliminate latency, drastically improving UI responsiveness and underlying data processing costs. - Cross‑Tenant Analytics:
Architect secure, privacy‑compliant solutions to detect widespread network or ISP issues by correlating cross‑tenant ML baseline scores.
- Roadmap Definition:
Partner closely with Product Management (PM) to define and champion the 6‑12 month AI roadmap, co‑developing business cases with clear ROI (e.g., faster MTTR, support deflection). - Code Quality & Infrastructure:
Act as the technical anchor for the team. Review code, establish best practices, improve infrastructure documentation, and "shift‑left" on regression testing to improve velocity. - Customer‑Centric Engineering:
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