Senior Staff Machine Learning Platform Engineer

### Who you are
- BS/MS or Ph.D. in Computer Science, a related technical field, or equivalent practical experience
- Professional experience in software engineering with a deep focus on MLOps, ML systems, or productionizing machine learning models at scale
- Expert-level programming skills in Python are required; experience in a systems language like Go, Java, or C++ is nice to have
- Deep, hands-on experience designing and building large-scale distributed systems on a major cloud platform (GCP, AWS, Azure, or OCI)
- Proven track record of leading the architecture of complex ML systems and MLOps pipelines using technologies like Kubernetes and Docker
- Mastery of ML frameworks (TensorFlow, PyTorch) and extensive experience with advanced inference optimization tools (ONNX, TensorRT)
- A strong understanding of popular model architectures (e.g., Transformers, CNNs, GNNs) is a significant plus
- Demonstrated expertise with modern LLM inference engines (e.g., vLLM, SGLang, TensorRT-LLM) is required. Open-source contributions in these areas are a significant plus
- Experience with low-level performance optimization, such as custom CUDA kernel development or using Triton Language, is a plus
- Experience with data infrastructure technologies (e.g., Kafka, Spark, Flink) is great to have
- Familiarity with CI/CD pipelines and automation tools (e.g., Jenkins, GitLab CI, Tekton) is a plus

### What the job involves
- With Prisma AIRS, Palo Alto Networks is building the world's most comprehensive AI security platform
- Organizations are increasingly building complex ecosystems of AI models, applications, and agents, creating dynamic new attack surfaces with risks that traditional security approaches cannot address
- In response, Prisma AIRS delivers model security, posture management, AI red teaming, and runtime protection
- Our customers can confidently deploy AI-driven innovation while ensuring a formidable security posture from development through runtime
- As a Principal Machine Learning Inference Engineer, you will serve as a technical authority and visionary for the Prisma AIRS team
- You will be responsible for the architectural design and long-term strategy of our AI platform - ML inference
- Beyond individual contribution, you will lead complex technical projects, mentor senior engineers, and set the standard for performance, scalability, and engineering excellence across the organization
- Your decisions will have a profound and lasting impact on our ability to deliver cutting-edge AI security solutions at a massive scale
- Architect and Design: Lead the architectural design of a highly scalable, low-latency, and resilient ML inference platform capable of serving a diverse range of models for real-time security applications
- Technical Leadership: Provide technical leadership and mentorship to the team, driving best practices in MLOps, software engineering, and system design
- Strategic Optimization: Drive the strategy for model and system performance, guiding research and implementation of advanced optimization techniques like custom kernels, hardware acceleration, and novel serving frameworks
- Set The Standard: Establish and enforce engineering standards for automated model deployment, robust monitoring, and operational excellence for all production ML systems
- Cross-Functional Vision: Act as a key technical liaison to other principal engineers, architects, and product leaders to shape the future of the Prisma AIRS platform and ensure end-to-end system cohesion
- Solve the Hardest Problems: Tackle the most ambiguous and challenging technical problems in large-scale inference, from mitigating novel security threats to achieving unprecedented performance goals

### Benefits
- Healthcare: We value your health and provide options so you can choose what best supports your lifestyle and personal health goals. Various nationwide plans — including Blue Shield of California, Kaiser Permanente, Delta Dental, VSP, and more — are available to you and your family. Our providers offer the flexibility you want and the coverage you need
- Wellness: With a large on-site gym and daily, instructor-led exercise classes at HQ in Santa Clara, Palo Alto Networks actively supports employee fitness. Our juice bar, Gym & Juice, features fresh juice blends, a quick and nutritious option pre- or post-workout. And our ongoing wellness speaker series highlights the latest trends in health and wellness
- Development: Professional development is serious business at Palo Alto Networks, where the Talent Development team offers 25+ courses to help employees boost their careers and develop leadership capability
- Financial: Most roles are eligible for equity grants, and we now offer new hires a quarterly vesting schedule right away. We want you to be fairly compensated and rewarded as soon as you make an impact – on our team, that happens immediately! And because we consider every employee a stakeholder in our long-term success, we offer an Employee Stock Purchase Plan (ESPP) with a 24-month lookback. From life insurance to health savings accounts, we provide ways to help you protect and grow your wealth. Palo Alto Networks offers both traditional and Roth 401(k) options, along with a company match of 50% to a max annual amount of $1,000
- Time Off: In addition to paid corporate holidays, Flexible Time Off programs empower our employees to balance their work schedules with personal time off. We offer 12 weeks of full pay for medical leave and 6 weeks of full pay for parental leave. As a company with strong ties to the military and veterans, we also offer 26 weeks of full pay for military leave
- Perks: Our corporate discount program provides employees with significant discounts on merchandise, travel and entertainment. We offer many on-site perks at headquarters, including cafeterias, fuel service, dry cleaning service, EV charging stations, and a company store. We also provide complimentary drinks, fresh fruit, and snacks in break rooms across our campus to help everyone power through the day.

Life at Palo Alto Network