Principal Machine Learning Platform Engineer (Prisma AIRS)

Ref ID: JR-020446

About the role

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.

Responsibilities

• 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.

Qualifications

• BS/MS or Ph.D. in Computer Science, a related technical field, or equivalent practical experience.
• Extensive 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 a plus.
• 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 a plus.
• Familiarity with CI/CD pipelines and automation tools (e.g., Jenkins, GitLab CI, Tekton) is a plus.

Pay

The starting base salary for this position is expected to be in the range of $157,200.00 - $254,100.00 per year. The offered compensation may also include restricted stock units and a bonus.

Benefits

A description of employee benefits may be found here.

This role is eligible for Immigration Sponsorship.