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Salary: USD 152,000 - 190,000 / annual
Zscaler is seeking a Staff Machine Learning Engineer to join the Artificial Intelligence Guard team. You will report directly to the Director of AI and Machine Learning Engineering and work at the core of the team that built the world's largest cloud security platform, processing over 400 billion transactions daily.
In this role, you will design, build, and deploy end-to-end machine learning pipelines while integrating advanced AI capabilities into production-ready SaaS offerings. Your responsibilities include:
- Building and deploying end-to-end ML pipelines spanning data curation, training/fine-tuning, evaluation, and high-scale serving
- Developing deep learning models for security use cases, including transformer-based classifiers, embedding models, and sequence modeling across high-volume traffic data
- Adapting, fine-tuning, and productionizing open-weight LLMs and small language models using techniques such as LoRA/QLoRA, instruction tuning, and distillation
- Optimizing 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
- Architecting and operationalizing robust ML services across cloud platforms (AWS, GCP) leveraging cloud-native microservice architectures
You should be someone who stays on top of the latest advancements in deep learning, thrives on uncovering complex patterns in large sparse datasets, and operates with an uncompromising sense of ownership. You are a proactive, independent problem solver energized by engineering elegant, resilient solutions for massive-scale technical challenges, with a growth mindset and continuous drive to learn. You excel at working cross-functionally with engineering teams to champion and execute organizational AI strategies.
REQUIREMENTS:
- Bachelor's or advanced degree in Computer Science, Machine Learning, Mathematics, Physics, Statistics, Engineering, or related field, with 2+ years of applied ML experience
- Demonstrated experience utilizing modern AI/ML frameworks and foundational model workflows to design, train, and deploy intelligent systems at scale
- 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) in production environments
- Ability to work with large-scale datasets, write clean, testable code, and operate with high autonomy on ambiguous technical challenges
PREFERRED QUALIFICATIONS:
- 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)
