Machine Learning Engineer

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.

• Build and deploy end-to-end ML pipelines spanning data curation, training/fine-tuning, evaluation, and high-scale serving
• Develop deep learning models for security use cases, including transformer-based classifiers, embedding models, and sequence modeling across high-volume traffic data
• Adapt, fine-tune, and productionize open-weight LLMs and small language models using techniques such as LoRA/QLoRA, instruction tuning, and distillation
• 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
• Architect and operationalize robust ML services across cloud platforms (AWS, GCP) leveraging cloud-native microservice architectures

• Demonstrated experience utilizing modern AI/ML frameworks and foundational model workflows to design, train, and deploy intelligent systems at scale
• Bachelor's or advanced degree in Computer Science, Machine Learning, Mathematics, Physics, Statistics, Engineering, or a related field, with 2+ years of applied ML experience
• Solid grounding in machine learning fundamentals, including loss functions, optimization, regularization, evaluation metrics, and handling imbalanced or noisy data
• Strong Python programming expertise and hands-on experience with modern deep learning frameworks such as PyTorch, TensorFlow, or JAX
• Proven experience training/fine-tuning large language models (LLMs) and deploying deep learning models (e.g., transformers, embedding models, generative AI architectures etc) in production environments and ability to work with large-scale datasets, write clean, testable code, and operate with high autonomy on ambiguous technical challenges
• U.S. citizenship due to the nature of the customers assigned to this role

• Remote capacity within the United States (with a hybrid preference for Santa Clara, CA)
• Base Pay Range: $152,000 - $190,000 USD