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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Sr Staff Machine Learning Engineer - **Company:** Palo Alto Networks - **Location:** Santa Clara, CA, United States - **Experience:** Expert - **Salary:** $141,000.0 - $228,075.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Airflow, C++ (Programming Language), Cyber Security, Software Debugging, Python (Programming Language), Machine Learning, Tensorflow, Software Systems, Pytorch, Large Language Models, Multi-Agent Systems, Deep Learning, Model Validation, Generative AI, Malware, Cyber Threat Analysis, Containerization, Kubernetes, Information Technology, Production Code, Malware Detection, Machine Learning Operations, Virtual Agents, Docker - **Published:** June 10, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=b464b2432a6c22f4 ## About the Role Do you have experience in Technical solutions implementation?, Do you have a Master's degree?, * Bachelor's, Master's, or Ph.D. in Computer Science, Machine Learning, Data Science, or a related field. * 4+ years of industry experience building, training, and deploying machine learning models into production environments. * Proven track record of taking ML projects from initial research/prototyping through to successful production rollout, particularly in the Cybersecurity domain. * Solid foundational knowledge of machine learning algorithms and deep learning architectures (e.g., Sequence models, GNNs, Transformers). * Strong proficiency in Python for ML development, with experience writing clean, scalable, and testable production code. * Familiarity with or willingness to work in Systems-level languages (e.g., C++, Go, or Rust) for performance-critical components. * Deep hands-on experience with PyTorch, TensorFlow, or other ML Frameworks. * Experience with MLOps Infrastructure, such as containerization (Docker, Kubernetes) and ML lifecycle tools (MLflow, Kubeflow, Airflow, or similar). * Ability to autonomously debug complex issues in both ML model performance and distributed software systems. * Clear and effective communication skills, with the ability to explain technical ML concepts to cross-functional partners. Preferred Qualifications * Experience with model evaluation, tuning, and handling imbalanced datasets (a common challenge in malware detection). * Bonus: Applied experience fine-tuning Large Language Models (LLMs) or building agentic AI workflows. ## Description We are seeking a Machine Learning Engineer to join our pioneering security team. This role is for a technical expert passionate about deconstructing complex threats and building the next generation of intelligent defense systems. You will be responsible for leading our efforts in leveraging machine learning and AI to detect and analyze emerging threats. You will also spearhead the design and implementation of innovative security solutions using generative AI, large language models (LLMs), and agentic systems to automate and scale our detection and response capabilities, keeping us ahead of sophisticated adversaries., * AI-Driven Detection & Automation: Lead end-to-end machine learning projects for threat detection. Design, build, and deploy innovative security solutions leveraging Generative AI and agentic systems. Develop intelligent agents and workflows to automate threat hunting, accelerate malware analysis, and streamline threat intelligence processes. * Research & Publication: Disseminate cutting-edge research findings and contribute to the security community by publishing results in technical blogs, industry white papers, and academic papers, particularly on topics related to malware analysis and AI in security * Collaboration & Communication: Work closely with cross-functional teams, including security researchers, engineers and product teams, to integrate your findings in reversing to product PoC and threat research. ## Related Videos - [Enhancing Workload Security in Kubernetes](https://www.wearedevelopers.com/videos/356-enhancing-workload-security-in-kubernetes) - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [Docker Compose: Rediscovered](https://www.wearedevelopers.com/videos/1978-docker-compose-rediscovered) - [Introduction to Azure Machine Learning](https://www.wearedevelopers.com/videos/368-introduction-to-azure-machine-learning) - [AI Model Management Life Circles: ML Ops For Generative AI Models From Research to Deployment](https://www.wearedevelopers.com/videos/1152-ai-model-management-life-circles-ml-ops-for-generative-ai-models-from-research-to-deployment) - [Full Spectrum File Uploads](https://www.wearedevelopers.com/videos/870-full-spectrum-file-uploads) ## Related Articles - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers)