Sr Staff Machine Learning Engineer (Xpanse)

Company Description

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 we’re looking for innovators who are as committed to shaping the future of cybersecurity as we are.

Our Approach to Work

We lead with flexibility and choice in all of our people programs. We have disrupted the traditional view that all employees have the same needs and wants. We offer personalization and offer our employees the opportunity to choose what works best for them as often as possible - from your wellbeing support to your growth and development, and beyond!

At Palo Alto Networks, we believe in the power of collaboration and value in-person interactions. This is why our employees generally work from the office three days per week, leaving two days for choice and flexibility to work where you feel most effective. This setup fosters casual conversations, problem-solving, and trusted relationships. While details may evolve, our goal is to create an environment where innovation thrives, with office-based teams coming together three days a week to collaborate and thrive, together!

Job Description

Your Career

We’re looking for a Senior Staff Machine Learning Engineer to join Cortex Xpanse. We help protect the world’s most essential organizations by finding and remediating risks on the Internet that no one else can find. 

Our team is responsible for attributing Internet assets to customer networks using a custom human-in-the-loop machine learning system while turning trillions of Internet data points into critical cybersecurity insights. The Asset Attribution ML-powered system helps provide customers with the world's most accurate view of their Internet-facing digital assets.

In this role, you will be an integral part of the group that continuously surveys petabytes of data to find risks online and protect some of the world's most important and relied upon organizations from malicious software and hackers. You will directly contribute to our mission by building AI/ML systems that increase the effectiveness of our security products, and you will directly work with stakeholders to define, design and implement improvements to our software.

You will be a technical leader within the team, developing features from end-to-end, and supporting our systems in production. You’ll leverage your data and software engineering knowledge to build new features, deploy data-driven automations, and ultimately augment our teams with ML-assisted workflows.

Your Impact

• Prototype, build, and improve AI systems for automating and augmenting team workflows

• Ship, support, debug, and secure AI systems in production

• Interact directly with security professionals who use your software and AI systems on a daily basis to increase their effectiveness and the quality of their work

• Improve ML scaling infrastructure for data ETL, model training and lifecycle, MLOps, batch and run-time processing, and inference

• Incorporate feature engineering, data pipelines, and transforms to deliver data for ML model training and run-time inference services

• Creatively analyze effectiveness of existing models to iterate and improve

Qualifications

Your Experience

• Experience with Python including delivering production-quality code and debugging in a production environment

• Experience with delivery and maintenance of AI systems and software in production environments 

• Strength in at least one of these areas:

  • MLOps (e.g. Feature Store, Model Serving, Model Monitoring)

  • Data Engineering (e.g. BigQuery, AirFlow, Apache Beam, Dataflow, SQL)

  • Designing and building data-driven applications backed by artificial intelligence (for example: recommendation systems, malware detection, behavior modeling, natural language processing, search, knowledge graphs, computer vision)

  • Applied ML techniques (e.g. gradient boosted decision trees, neural networks, NLP, LLMs) and ML frameworks (e.g. scikit-learn, tensorflow, pytorch, Hugging Face, MLFlow)

• Conceptual thinking and creativity; you demonstrate an ability to consider various techniques to solve different modeling problems we face

• Ability to collaborate and to convey complex technical concepts to both technical and non-technical stakeholders

Additional Information

The Team

Unit 42 Consulting is Palo Alto