Sr Staff Machine Learning Engineer

Palo Alto Networks•San Jose, CA

1d•Hybrid

About The Position

At Palo Alto Networks, the mission is to protect the digital way of life by solving real-world problems with cutting-edge technology and bold thinking. The company values Disruption, Collaboration, Execution, Integrity, and Inclusion, and integrates AI into its operations. This role is for a Machine Learning Engineer to join a pioneering security team, focusing on deconstructing complex threats and building the next generation of intelligent defense systems. The engineer will be responsible for leading efforts in leveraging machine learning and AI to detect and analyze emerging threats. This includes spearheading the design and implementation of innovative security solutions using generative AI, large language models (LLMs), and agentic systems to automate and scale detection and response capabilities, keeping the company ahead of sophisticated adversaries. Collaboration is emphasized, with most teams working from the office full time, with flexibility when needed.

Requirements

Bachelor’s, Master’s, or Ph.D. in Computer Science, Machine Learning, Data Science, or a related field.
6+ 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.

Nice To Haves

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.

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.