Senior Machine Learning Engineer

Palo Alto Networks; United States; On-site; USD 120,000 - 180,000; Full time

Job summary

Palo Alto Networks is seeking a Machine Learning Engineer to join the Internet Security Research Team in the United States. You will collaborate with data scientists and security researchers to develop ML solutions for detecting and defending against evolving web threats.

The role emphasizes building scalable ML systems, training pipelines, and deploying models to process large volumes of URLs, with a start date around August 2026 as part of the 2026 New Hire cohort.

Qualifications

• MS or PhD in Machine Learning, Computer Science, Data Science, or a related field, with graduation expected between December 2025 and July 2026
• Proficiency in at least one programming language such as Python, Java, or Golang
• Experience applying supervised and/or unsupervised machine learning algorithms on various data types
• Desirable: NLP techniques or document classification
• Desirable: Transformers and convolutional networks
• Desirable: Cloud platforms (GCP, AWS) and Docker/Kubernetes
• Desirable: Design, implement, and deploy system components including regression and integration testing

Responsibilities

• As a Machine Learning Engineer on our Internet Security Research Team, you will be a key innovator transforming ideas into products for our next-generation security platform
• You will work with data scientists and security researchers to implement projects that detect and defend against emerging web security threats
• Start date in August 2026 as part of the 2026 New Hire cohort
• Perform in-depth data analysis to deeply understand security data and the threat domain
• Design, train, and evaluate machine learning algorithms to significantly improve the analytical performance of threat detection models
• Build and productionize machine learning models and develop the distributed systems that utilize them to analyze and categorize enormous volumes of URLs
• Design and build robust, scalable machine learning systems, balancing cost with model performance
• Establish automated training pipelines and develop data analytics tools to incrementally enhance model performance on a growing dataset
• Collaborate with data scientists, security researchers, and Product Managers to gather requirements, design, and implement systems
• Challenge existing approaches to simplify complex systems and improve efficiency
• Work with engineers and SREs on release, deployment, and operational processes

Skills

Programming languages

Education

MS/PhD in ML/CS/Data Science

Tools

Docker

Kubernetes

Job description

Requirements

• MS or PhD in Machine Learning, Computer Science, Data Science, or a related field, with graduation expected between December 2025 and July 2026
• Proficiency in at least one programming language such as Python, Java, or Golang
• Experience applying supervised and/or unsupervised machine learning algorithms on various data types
• (Desirable) Working knowledge of Natural Language Processing (NLP) techniques or document classification
• (Desirable) Familiarity with advanced machine learning architectures like transformers and convolutional networks
• (Desirable) Experience with cloud platforms (GCP, AWS) and container-based development (Docker, Kubernetes)
• (Desirable) Ability to design, implement, and deploy system components, including regression and integration testing

What the job involves

• As a Machine Learning Engineer on our Internet Security Research Team, you will be a key innovator transforming ideas into products for our next-generation security platform
• You will work with data scientists and security researchers to implement projects that detect and defend against emerging web security threats
• This role is part of our dedicated 2026 New Hire cohort, with an anticipated start date in August 2026
• Perform in-depth data analysis to deeply understand security data and the threat domain
• Design, train, and evaluate machine learning algorithms to significantly improve the analytical performance of threat detection models
• Build and productionize machine learning models and develop the distributed systems that utilize them to analyze and categorize enormous volumes of URLs
• Design and build robust, scalable machine learning systems, carefully balancing cost with model performance
• Establish automated training pipelines and develop data analytics tools to incrementally enhance model performance on a growing dataset
• Proactively collaborate with data scientists, security researchers, and Product Managers to gather requirements, design, and implement systems
• Challenge existing approaches curiously and positively to simplify complex systems and improve efficiency
• Work effectively with other engineers and SREs on release, deployment, and operational processes, ensuring alignment and accountability