Staff Machine Learning Engineer

<div class="content-intro"><p data-pm-slice="1 1 []">Zscaler (NASDAQ: ZS) accelerates digital transformation so customers can be more agile, efficient, resilient, and secure. The Zscaler Zero Trust Exchange platform protects thousands of customers from cyberattacks and data loss by securely connecting users, devices, and applications in any location. Distributed across 200+ public data centers globally and thousands of private sites at the edge, the SASE-based Zero Trust Exchange is the world’s largest in-line cloud security platform.</p> <p>We believe the future of work is Human + AI and are building an AI-native enterprise where human potential is amplified by machine intelligence to solve the world’s hardest security challenges. Driven by deep customer obsession, we are committed to the mission, outcome, and to each other. We bring these commitments to life through three core behaviors: ownership and collaboration, trust through outcomes and impact, and a challenge culture with ongoing feedback. Ready to make an impact at the company pioneering security transformation in the AI era? Join us at Zscaler.</p></div><h3><strong>Role</strong></h3> <p>We are looking for a Machine Learning Engineer to join our Artificial Intelligence Guard team in a remote capacity within the United States (with a hybrid preference for Santa Clara, CA), reporting directly to the Director of AI and Machine Learning Engineering team. Operating at the core of the team that built the world’s largest cloud security platform processing over 400 billion transactions daily, you will design, build, and deploy end-to-end machine learning pipelines while integrating advanced AI capabilities into production-ready SaaS offerings to directly impact our global strategic roadmap.</p> <h3><strong>What You’ll Do (Role Expectations)</strong></h3> <ul> <li>Build and deploy end-to-end ML pipelines spanning data curation, training/fine-tuning, evaluation, and high-scale serving</li> <li>Develop deep learning models for security use cases, including transformer-based classifiers, embedding models, and sequence modeling across high-volume traffic data</li> <li>Adapt, fine-tune, and productionize open-weight LLMs and small language models using techniques such as LoRA/QLoRA, instruction tuning, and distillation</li> <li>Optimize models for production through quantization, batching, and high-throughput serving with frameworks like Hugging Face, PEFT, vLLM, TensorRT-LLM, and ONNX Runtime to balance latency, cost, and quality</li> <li>Architect and operationalize robust ML services across cloud platforms (AWS, GCP) leveraging cloud-native microservice architectures</li> </ul> <h3><strong>Who You Are (Success Profile)</strong></h3> <ul> <li>You enjoy being on top of the latest advancements and research in the deep learning space and you learn fast.</li> <li>You thrive on uncovering complex patterns within large, sparse datasets, leveraging a rigorous, highly numerate background to solve multifaceted business problems.</li> <li>You operate with an uncompromising sense of ownership and an execution-focused mindset, seamlessly bridging the gap between theoretical modeling and production deployment.</li> <li>You are a proactive, independent problem solver energized by engineering elegant, resilient solutions for massive-scale technical challenges.</li> <li>You possess a growth mindset and a continuous drive to learn, actively adapting to and implementing cutting-edge machine learning advancements.</li> <li>You are a collaborative partner who excels at working cross-functionally alongside engineering teams to champion and execute organizational AI strategies.</li> </ul> <h3><strong>What We’re Looking For (Minimum Qualifications)</strong></h3> <ul> <li>Demonstrated experience utilizing modern AI/ML frameworks and foundational model workflows to design, train, and deploy intelligent systems at scale</li> <li>Bachelor's or advanced degree in Computer Science, Machine Learning, Mathema