Staff Machine Learning Engineer

Zscaler•Santa Clara, CA

5h•$152,000 - $190,000•Hybrid

About The Position

Zscaler is looking for a Machine Learning Engineer to join their Artificial Intelligence Guard team. This role will be responsible for designing, building, and deploying end-to-end machine learning pipelines and integrating advanced AI capabilities into production-ready SaaS offerings. The position operates within the core team of the world's largest cloud security platform, processing over 400 billion transactions daily, and directly impacts the company's global strategic roadmap. The role is remote within the United States, with a hybrid preference for Santa Clara, CA, and reports to the Director of AI and Machine Learning Engineering.

Requirements

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
Ability to work with large-scale datasets, write clean, testable code, and operate with high autonomy on ambiguous technical challenges

Nice To Haves

Proven experience developing generative AI architectures or building scalable inference optimization pipelines
Peer-reviewed publications or prominent open-source contributions demonstrating depth in modern deep learning techniques (e.g., NeurIPS, ICML, ICLR, ACL, EMNLP, NAACL, IEEE)

Responsibilities

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