Our MissionAt 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 AreIn 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 SummaryYour CareerAs a Principal Machine Learning Engineer, you will provide technical leadership in architecting and delivering next-generation, AI-driven cloud security solutions. In this role, you will drive scalable cloud security architecture through hands-on system design and coding, build and fine-tune advanced AI/ML, Large Language Models (LLMs), and Small Language Models (SLMs), oversee the full product lifecycle, and collaborate cross-functionally to transform complex security telemetry into high-reliability Security-as-a-Service (SECaaS) offerings.
Your Impact- Architect Agentic Systems: Lead the design and implementation of ML/GenAI-powered agents featuring dynamic planning, multi-step execution, self-correction, and seamless multi-agent and human-in-the-loop collaboration.
- Model Development, Diagnosis & Fine-Tuning: Build, evaluate, and fine-tune both LLMs and SLMs, alongside multimodal architectures. Diagnose model regressions or sub-par performance, pinpoint root causes (data distribution shift, alignment failures, loss anomalies), and implement targeted fine-tuning (SFT, DPO/RLHF, PEFT/LoRA) to drive continuous quality recovery and improvement.
- GPU & System Optimization: Profile, debug, and optimize model training and inference on GPU clusters, addressing memory allocation bottlenecks (OOMs), distributed communication overhead, and compute utilization.
- Data Pipelines & Telemetry Integration: Architect scalable data preprocessing and feature extraction pipelines to feed real-time endpoint telemetry into agentic and analytical workflows.
- Production Engineering & Standards: Drive end-to-end engineering from prototype to enterprise-grade production, establishing best practices for prompt architecture, agent memory persistence, context window optimization, tool-use execution, and safety guardrails.
- Technical Strategy: Continuously evaluate state-of-the-art agent frameworks, model serving engines, and accelerator architectures to inform long-term technical roadmaps.Your
Qualifications Your Experience- Experience: 7+ years in professional software engineering, with a proven track record of deploying production-grade ML, GenAI, or agentic architectures at enterprise scale.
- Languages & Backend: Advanced proficiency in Python (AI/ML development) paired with production-level Go or Java experience for building distributed microservices.
- Data Pipeline Engineering: Strong hands-on capabilities building and scaling data pipelines. Proficient with Apache Spark, core data tools, SQL, BigQuery schema design, and Google Cloud infrastructure (Dataproc, GKE, and GCP SDKs).
- LLM/SLM Operations & Fine-Tuning: Practical operational experience with both LLMs and SLMs. Deep expertise in diagnosing model performance issues and executing remedial fine-tuning strategies (dataset curation, parameter-efficient fine-tuning, loss monitoring, alignment, and evaluation benchmarks).
- Hardware & Accelerator Debugging: Practical experience with GPU profiling and debugging (e.g., CUDA memory optimization, multi-GPU synchronization, batch throughput tuning, and handling GPU compute crashes/stalls).
- Core ML Foundations: Solid understanding of Retrieval-Augmented Generation (RAG), vector storage, prompt optimization, model evaluation metrics, and applied statistical modeling.
- Education: BS or MS in Computer Science, Data Science, Software Engineering, or a related technical discipline.
Preferred Qualifications (Bonus)- Domain Knowledge: Background in cybersecurity, threat detection, or cloud security principles is a strong bonus.
- Networking: Working knowledge of L3-L7 networking protocols, packet inspection, and network security topologies.
- Telemetry: Experience handling high-volume cybersecurity telemetry, endpoint detection data, or real-time threat intelligence feeds.
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Compensation DisclosureThe compensation offered for this position will depend on qualifications, experience, and work location. For candidates who receive an offer at the posted level, the starting base salary (for non-sales roles) or base salary + commission target (for sales/com-missioned roles) is expected to be the annual range listed below. The offered compensation may also include restricted stock units and a bonus. A description of our employee benefits may be found here.
$163,200.00 - $264,000.00/yr
Our Commitment We're trailblazers that dream big, take risks, and challenge cybersecurity's status quo. It's simple: we can't accomplish our mission without diverse teams innovating, together.
We are committed to providing reasonable accommodations for all qualified individuals with a disability. If you require assistance or accommodation due to a disability or special need, please contact us at accommodations@paloaltonetworks.com.
Palo Alto Networks is an equal opportunity employer. We celebrate diversity in our workplace, and all qualified applicants will receive consideration for employment without regard to age, ancestry, color, family or medical care leave, gender identity or expression, genetic information, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran status, race, religion, sex (including pregnancy), sexual orientation, or other legally protected characteristics.
All your information will be kept confidential according to EEO guidelines.
Is role eligible for Immigration Sponsorship?: Yes
