Senior Staff Machine Learning Engineer

As a Senior Staff Machine Learning Engineer at Zscaler, your key responsibilities include building and maintaining large-scale distributed systems that support the entire machine learning pipeline. This involves everything from data collection and feature engineering to model training, evaluation, and real-time serving. You will also need to develop functional specifications, assess task requirements, and collaborate with various stakeholders to solve complex real-world business issues, especially in areas like threat detection and automation.

Zscaler requires candidates to have a minimum of 10 years of experience in software engineering or as a machine learning platform engineer. You should be well-versed in algorithms, distributed data processing tools like Spark or Apache Beam, and possess a deep understanding of cloud infrastructures such as AWS or GCP suited for AI workloads. Additional preferred qualifications include experience with end-to-end application design and implementation, familiarity with multi-agent systems, and contributions to open-source ML projects.

Zscaler is deeply committed to diversity, equity, inclusion, and belonging. We believe that diverse teams drive innovation and success, and we ensure that our hiring process reflects these principles. All qualified applicants, regardless of race, gender, religion, or any other characteristic, will receive equal consideration for employment. Our inclusive approach is an integral part of who we are as a company and is vital in building a team that represents various backgrounds and identities.

In the role of Senior Staff Machine Learning Engineer at Zscaler, you can expect to work on exciting and innovative projects that include developing and refining machine learning algorithms for real-time threat detection and business intelligence applications. Projects may involve building out scalable data processing infrastructure and leveraging advanced ML techniques to enhance our cloud security solutions, making a tangible impact on how enterprises operate in a digital world.

Zscaler offers an extensive benefits program designed to support the diverse needs of our employees and their families. This includes various health plans, vacation and sick leave, parental leave, retirement options, and education reimbursement. As part of our culture, we also ensure that our employees enjoy in-office perks and a well-rounded work-life balance to foster personal and professional growth.

When answering this question, illustrate your previous projects involving large-scale distributed systems. Highlight specific tools or frameworks you've used, such as Apache Spark or BigQuery, and detail any unique challenges you faced and how you overcame them. Show your understanding of system architecture and the complexities involved in building and maintaining such environments.

Be prepared to discuss a variety of machine learning frameworks you have experience with, like TensorFlow, PyTorch, or Scikit-learn. Discuss the projects where you've applied these frameworks, specifying any particular algorithms or use cases and how they contributed to your success in solving business problems.

In your response, describe your systematic approach to debugging models, including steps such as reviewing data preprocessing, assessing feature engineering, visualizing model performance metrics, and adjusting hyperparameters. Discuss any specific tools or techniques that you've found useful in diagnosing model issues.

This is a chance to showcase a specific case where you applied machine learning to address a business challenge. Detail the problem, your approach, the algorithms used, and the resulting impact on the organization. Emphasizing measurable outcomes will strengthen your response.

Share your hands-on experiences working with cloud platforms such as AWS or GCP in relation to machine learning projects. Discuss specific services you have utilized, like AWS SageMaker or Google Cloud ML Engine, and emphasize how you implemented and optimized these cloud resources for AI workloads.

Discuss your strategies for ensuring reliability and performance, such as implementing automated testing, conducting regular model evaluations, monitoring for drift, and refining models based on new data or changing conditions. Your answer should reflect a proactive approach to maintaining high-quality ML systems.

Be detailed about any experience you have with anomaly detection, including specific algorithms you’ve employed. Talk about the contexts in which you have implemented these systems and how they helped in identifying unusual patterns or behaviors that benefited the business.

Articulate what prompt engineering involves, especially in the context of large language models. Provide examples of how you have applied prompt engineering to optimize the performance of models, demonstrating your awareness of nuances and practical applications in real scenarios.

If applicable, share your contributions and experiences in open-source communities. Discuss the projects you contributed to, your specific role, and any challenges faced during the contribution process. Highlight the importance of collaboration and community in the machine learning field.

Reflect on a specific project that posed significant challenges, whether in terms of data scale, complexity of algorithms, or integration with other systems. Explain how you navigated these challenges, the innovative approaches you employed, and the end results. Be honest about learning experiences as well.