Sr. Staff Machine Learning Engineer - Data Lake, Anomaly Detection

Zscaler•San Jose, CA

8d•Hybrid

About The Position

We are looking for a Sr. Staff Software Engineer to join our Zscaler Digital Experience (Core Intelligence and Data) team. This is a hybrid role based in San Jose, CA 3 days a week, reporting to the Sr. Manager, Software Engineering. You will join the team responsible for building the world’s largest cloud security platform, helping us enhance services and increase our global footprint. You will play a pivotal role in enabling organizations worldwide to harness speed and agility through a cloud-first strategy, leveraging our multitenant architecture that serves over 15 million users.

Requirements

BS in Computer Science with 8+ years of experience, or MS/PhD with 5+ years of experience solving real-world problems using AI/ML and distributed systems
Proficiency in programming, data structures, algorithms, and machine learning, with exceptional problem-solving skills driven by first-principles thinking
Hand-on experience with AI modeling, including feature generation, prompt engineering, evaluations and productionization
Experience in the full lifecycle of ML models, including building, deployment, monitoring, and optimization
Expertise in designing and operating distributed microservices using tools like Kubernetes and Docker, and writing production-grade code in Python, Go, or Java

Nice To Haves

Experience fine-tuning and deploying proprietary SLMs/LLMs at scale, with a focus on optimizing latency, cost, safety, and evaluations
Experience delivering production-ready AI systems, including expertise in abnormally detection, event correlation and incident investigation
Proven ability to design and implement high-performance, resilient systems with well-defined service-level objectives

Responsibilities

Own agentic trouble shooting framework, framing high-impact use cases, designing workflows and playbooks, and building processes for all products
Evaluate and integrate state-of-the-art GenAI advances to deliver reliable and cost-efficient production features, utilizing LLMs, various machine learning models, data processing, fine-tuning, and inference optimization
Work with the world class cloud platform and data lakes for feature exploration and generation
Handle volume data with the real time pipeline for data processing and aggregation
Design, implement, and operate scalable production systems, specifically focusing on microservices, data pipelines, orchestration, and caching