\\\\Our Mission\\\\ At Palo Alto Networks® everything starts and ends with our mission: Being the cybersecurity partner of choice, protecting our digital way of life. Our vision is a world where each day is safer and more secure than the one before. We are a company built on the foundation of challenging and disrupting the way things are done, and were looking for innovators who are as committed to shaping the future of cybersecurity as we are. \\\\Who We Are\\\\ We believe collaboration thrives in person. Thats why most of our teams work from the office full time, with flexibility when its needed. This model supports real-time problem-solving, stronger relationships, and the kind of precision that drives great outcomes. \\\\Your Career\\\\ You will build machine learning models and develop big data and distributed systems that use the models to analyze and categorize an enormous amount of URLs. You will be a key person in transforming ideas into products which are part of the next generation security platform. The Internet Security Research Team is responsible for innovating new security techniques. \\\\Your Impact\\\\ \\+ Design, build, and operate production machine learning systems that balance model quality, cost, latency, and reliability in a security-sensitive environment. \\+ Own the end-to-end lifecycle of ML and LLM components, from problem formulation and model development to production deployment, monitoring, and iterative improvement. \\+ Integrate ML and LLM-based services with backend systems and data pipelines, ensuring scalability, observability, and safe operation in production. \\+ Develop and maintain automated training, evaluation, and retraining pipelines, and build data analysis tools to continuously improve model performance as data and threats evolve. \\+ Partner closely with Product Managers and domain experts to translate product and security requirements into robust ML solutions with clear success metrics. \\+ Collaborate with software engineers and SREs on release planning, deployment strategies, monitoring, and incident response to ensure reliable and predictable production behavior. \\\\Your Experience\\\\ \\+ Strong problem solver with collaborative team player with clear communication skills, able to work effectively across engineering, product, and SRE teams. \\+ Solid foundation in Machine Learning, Deep Learning, and NLP, with hands-on experience using modern architectures such as transformer-based models and representation learning techniques. \\+ Practical experience applying Large Language Models (LLMs) to real-world problems, including text understanding, classification, extraction, summarization, or reasoning over large-scale and noisy data. \\+ Experience designing, implementing, and operating LLM-powered components in production, including prompt design, model adaptation or fine-tuning, evaluation, and cost/performance optimization. \\+ Familiarity with AI agentbased approaches, such as multi-step inference pipelines, tool-augmented LLM workflows, or systems that combine models, heuristics, and external signals to drive reliable decisions. \\+ Experience with MLOps / AIOps practices for operating ML and LLM systems in production, including model lifecycle management, monitoring, logging, alerting, retraining workflows, and debugging production issues. \\+ Understanding of model quality, robustness, and safety considerations, including evaluation methodologies, failure modes, and guardrails required for production ML systems in security-sensitive environments. \\+ Strong experience with ML frameworks, libraries, and tooling (e.g., PyTorch, Tensorflow, Keras, Scikit-learn, Kubeflow), and solid software engineering fundamentals. \\+ Ability to independently own ML features end-to-end, from problem formulation and system design to implementation, deployment, and iterative improvement in production. \\+ Experience with website content understanding, website classifications, security, or large-scale internet data is a strong plus. \\+ Proficient in Python, working knowledge of Java, Linux, and shell scripting. \\+ Experience building and operating services on cloud platforms (GCP and/or AWS) and in containerized environments (Docker, Kubernetes). \\+ Familiarity with relational and NoSQL data stores such as MySQL, MongoDB, or similar systems. \\+ Experience applying LLMs and agentic systems in security-sensitive or high-precision domains is a strong plus. \\+ MS or Ph.D. in Computer Science or a related field, with a focus on Machine Learning, and 2+ years of industry experience delivering ML systems in production environments. \\\\The Team\\\\ As a member of the Internet Security Research Team specifically Advanced URL Filtering Data Science, you will work closely with data scientists, security researchers, and other engineers on implementing different projects to detect and defend against various emerging threats in the areas of Web Security. \\\\Compensation Disclosure\\\\ The compensation offered for this position 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/commissioned roles) is expected to be between $205,000 - $235,000/YR. The offered compensation may also include restricted stock units and a bonus. A description of our employee benefits may be found here () . \\\\Our Commitment\\\\ Were problem solvers that take risks and challenge cybersecuritys status quo. Its simple: we cant accomplish our mission without diverse teams innovating, together. We are committed to providing reasonable accommodations for all qualified individuals with a disability. If you require assistance or accommodation due to a disability or special need, please contact us at . 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. All your information will be kept confidential according to EEO guidelines.

The role requires a strong problem solver with collaborative team player skills and clear communication abilities. A solid foundation in Machine Learning, Deep Learning, and NLP is necessary, with hands-on experience in modern architectures like transformer-based models and representation learning techniques. Practical experience applying Large Language Models (LLMs) to real-world problems is expected, including text understanding, classification, extraction, summarization, or reasoning over large-scale and noisy data. Experience designing, implementing, and operating LLM-powered components in production, including prompt design, model adaptation or fine-tuning, evaluation, and cost/performance optimization, is also required. Familiarity with MLOps/AIOps practices for operating ML and LLM systems in production is needed, along with strong experience with ML frameworks, libraries, and tooling. An MS or Ph.D. in Computer Science or a related field, with a focus on Machine Learning, and at least two years of industry experience delivering ML systems in production environments are required.