> Markdown version of [/jobs/ext/3595061-machine-learning-engineer](https://www.wearedevelopers.com/jobs/ext/3595061-machine-learning-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# Machine Learning Engineer

- **Company:** Zscaler, Inc.
- **Location:** Santa Clara, CA, United States (Remote available)
- **Experience:** Experienced
- **Salary:** $152,000.0 - $190,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Amazon Web Services, Big Data, Software as a Service, Cloud Computing Security, Cloud Engineering, Python (Programming Language), Machine Learning, Language Modeling, Tensorflow, Azure Machine Learning, Software Deployment, QLoRA, Pytorch, Transfer Learning, Large Language Models, Deep Learning, Generative AI, Loss Functions, Build Management, Information Technology, ONNX (Open Neural Network Exchange) Format, HuggingFace, Machine Learning Operations, TensorRT, Multiaccess Edge Computing, VLLM, Microservices
- **Published:** October 6, 2026
- **Apply:** https://startup.jobs/staff-machine-learning-engineer-zscaler-company-10299441

## About the Role

* You enjoy being on top of the latest advancements and research in the deep learning space and you learn fast.
 * You thrive on uncovering complex patterns within large, sparse datasets, leveraging a rigorous, highly numerate background to solve multifaceted business problems.
 * You operate with an uncompromising sense of ownership and an execution-focused mindset, seamlessly bridging the gap between theoretical modeling and production deployment.
 * You are a proactive, independent problem solver energized by engineering elegant, resilient solutions for massive-scale technical challenges.
 * You possess a growth mindset and a continuous drive to learn, actively adapting to and implementing cutting-edge machine learning advancements.
 * You are a collaborative partner who excels at working cross-functionally alongside engineering teams to champion and execute organizational AI strategies., * 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, * 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)

#LI-Remote #LI-YC2

## Description

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., * 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