Sr Staff Machine Learning Engineer

Job Summary 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. Key Responsibilities 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. 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. 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. Required Qualifications 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). Applied experience fine‑tuning Large Language Models (LLMs) or building agentic AI workflows. Compensation Disclosure Compensation will depend on qualifications, experience, and work location. For candidates who receive an offer at the posted level, the starting base salary (for non‑sales roles) or base salary + commission target (for sales/com‑missioned roles) is expected to be the annual range listed below. The offered compensation may also include restricted stock units and a bonus. $141,000.00 - $228,075.00/yr Immigration Sponsorship Yes Accommodation Statement If you require assistance or accommodation due to a disability or special need, please contact us at View email address on click.appcast.io. Equal Opportunity Statement Palo Alto Networks is an equal opportunity employer. We celebrate diversity in our workplace, and all qualified applicants will receive consideration for employment without regard to age, ancestry, color, family or medical care leave, gender identity or expression, genetic information, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran status, race, religion, sex (including pregnancy), sexual orientation, or other legally protected characteristics. Privacy Statement All your information will be kept confidential according to EEO guidelines. #J-18808-Ljbffr