Sr. Staff Machine Learning Engineer - job 1 of 3

As a Sr. Staff Machine Learning Engineer at Zscaler, your core responsibilities include leading the design and development of ML components and cybersecurity applications, providing technical guidance to junior engineers, optimizing machine learning pipelines, and collaborating with cross-functional teams to address key cybersecurity challenges. You will also experiment with advanced algorithms to enhance our existing solutions.

To qualify for the Sr. Staff Machine Learning Engineer role at Zscaler, you should have a minimum of 7 years of experience in machine learning, a strong proficiency in Python and SQL, and extensive knowledge of data modeling and feature engineering. A Bachelor's degree in fields related to computer science, physics, or mathematics is required, while advanced degrees like a Master's or PhD are preferred.

A strong background in unsupervised learning techniques, experience in developing scalable algorithms, and familiarity with public cloud services such as AWS or Google Cloud will make you stand out for the Sr. Staff Machine Learning Engineer position at Zscaler. Additionally, skills in Rust programming and an understanding of model explainability will enhance your application.

Yes, the Sr. Staff Machine Learning Engineer position at Zscaler is hybrid, requiring employees to work in the San Jose office for three days a week. This setup allows for a blend of in-person collaboration and remote work flexibility.

The salary for the Sr. Staff Machine Learning Engineer position at Zscaler ranges from $147,000 to $210,000 USD. This range is influenced by factors such as skills, experience, and relevant education.

When answering this question, highlight specific projects or models you've worked on, focusing on your role and the technologies you used. Be sure to include details about the outcomes achieved, explaining how your contributions positively impacted the project's success.

Choose a specific algorithm you are familiar with, such as Neural Networks or Decision Trees. Break it down into simple terms, explain its purpose, and discuss how you implemented it in a previous project, showcasing any challenges faced and results obtained.

Discuss your approach to validation techniques, including cross-validation and error analysis. Explain the importance of selecting performance metrics and how you iterate on model enhancements based on data-driven insights to improve accuracy.

Mention the tools you're most comfortable with, such as TensorFlow, Keras, or PyTorch, and discuss why you prefer them. Highlight any experience with cloud-based solutions like AWS or Azure that you've integrated into your workflow.

Outline your process for selecting relevant features, including both domain knowledge and statistical methods. Provide examples of how effective feature engineering has positively impacted the performance of your previous models.

Discuss specific algorithms you have used for anomaly detection, such as Isolation Forest or Support Vector Machines. Explain how you apply them in a cybersecurity context and share any relevant results or case studies.

Emphasize your proactive approach to learning, including following journals, attending conferences, participating in webinars, or enrolling in online courses. Mention specific sources you find reliable and insightful.

Provide a detailed and structured response, including the problem context, the analysis you conducted, the steps you took to resolve it, and the ultimate outcome. Focus on demonstrating your problem-solving skills and adaptive thinking.

Share your experiences working with teams from different disciplines, emphasizing your communication skills and ability to align objectives. Discuss how clear, proactive communication has led to successful project outcomes.

Express your enthusiasm for the innovative possibilities within the intersection of machine learning and cybersecurity. Discuss specific technologies or breakthroughs you are passionate about and how they can potentially transform security measures.