Senior Machine Learning Engineer (Causality)

Company Description

Our Mission

At Palo Alto Networks® everything begins and concludes with our mission:

Becoming 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 challenging and disrupting the way things are done, and we’re looking for innovators as committed to shaping the future of cybersecurity as we are.

FLEXWORK is an employee-centric reimagining of how we work. We built FLEXWORK based on employee feedback - it focuses on flexibility, trust, and choice whenever possible. It’s a journey of disruption that has yielded the best of our values. We offer as much flexibility as possible, with benefits that meet your needs and learning opportunities you feel passionate about.

Our Approach to Work

At Palo Alto Networks, we believe in the power of collaboration and the value of in-person interactions. Therefore, 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

You will have the opportunity to build a career applying Machine Learning and AI to various challenges surrounding Firewalls and Network Security Operations in general.

Your work will range from Time series techniques such as Anomaly Detection and forecasting, Causality techniques for Root Cause Analysis, all the way to recent advances like Generative AI, presenting you with exciting challenges.

You will assist in creating AI solutions on top of one of the biggest data lakes with exabytes of data and hundreds of thousands of devices constantly sending telemetry.

At Palo Alto Networks, Senior Machine Learning Engineers are:

• Experts in specific areas of statistics, machine learning, or AI in general.
• Committed professionals who deliver ML features with solid evidence and support provided by strong data analysis practices and outcomes.
• Analytical engineers that base their decisions and designs on data.
• Individuals who are curious about the latest technologies, always learning new techniques, and driven by a profound understanding of problems.

Your Impact

• You will work in a fast-paced team to create and deliver new features in a product that many customers use daily to ensure their network operation is healthy and secure.
• You will be part of a team using ML and AI to redefine how Network Security operations are conducted.
• You will assist in designing and developing AI/ML frameworks that allow us to apply algorithms at the scale of hundreds of thousands of devices reporting thousands of metrics on a minute basis.
• You will collaborate in the design of user experiences that allow users to consume profound and complex insights in an easy and manageable way.
• You will play a key role in the design, development, and implementation of causal inference (and causal discovery) techniques across the product.

Qualifications

Your Experience

• Formal background and experience in both causal inference methods and causal discovery approaches, including classic techniques like Propensity Score, matching, difference in difference, etc., and new techniques like double machine learning or Potential Outcomes.
• A Master's or PhD degree in Econometrics, Computer Science, Mathematics, Statistics or a related field.
• 2+ years of industry experience (or equivalent education + experience) in Machine Learning techniques and data analysis. Experience with Time series, Causality, and NLP is a plus.
• 2+ years of experience in design, algorithms, and data structures - Proficiency with one or more of the following languages is required - Java, Python.
• A proven ability to go beyond ML algorithms and models by contributing to the design and implementation of services and pipelines that expose ML artifacts.
• Experience with deep learning frameworks such as PyTorch and TensorFlow, Huggingface Transformers, or related is a plus.
• Hands-on experience with causal inference libraries and frameworks.
• The ability to become a self-driven individual contributor and an excellent team player when the teams and the business require it.

Additional Information

The Team

We aim to build the industry's best AIOps product that enables our customers to maintain a healthy and secure network, thanks to AI and ML.

Our engineering team is at the heart of applying AI on a large scale - challenging how we, and the industry, consider network security operations.

We constantly hunt for the next big challenge to produce maximum value for our customers. And that's why we need individuals who feel comfortable in ambiguity and are willing to be creative in finding the best balance between innovation and speed.

Our Commitment

We’re trailblazers that dream big, take risks, and challenge the cybersecurity’s status quo. It’s simple: we can’t 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 [email protected].

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 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 will depend on the job market value for this type of work.