Cluster Monitoring Software – Engineering Lead

As the Cluster Monitoring Software – Engineering Lead at Cerebras Systems, you'll be responsible for building a world-class monitoring solution for our AI cluster infrastructure, which includes managing the performance of hundreds of wafer-scale accelerator systems and thousands of servers. Your responsibilities will involve technical leadership, collaborating with users to understand their needs, and developing a strategic roadmap for the monitoring software. You’ll also be tasked with assembling a talented engineering team that delivers an intuitive and efficient management tool.

Candidates for the Cluster Monitoring Software – Engineering Lead position at Cerebras Systems should have at least 3 years of demonstrated engineering leadership in distributed systems monitoring. A proven track record of product delivery and deploying distributed solutions is essential. Additionally, strong communication skills, the ability to act like a stakeholder, and a background in building observability/monitoring software are highly desired. Proficiency in Kubernetes and knowledge of bare metal cluster management will further strengthen your application.

The Cluster Monitoring Software at Cerebras Systems is crucial for ensuring the smooth operation of our AI clusters. It provides operators with essential insights and metrics related to cluster performance, enabling quick incident resolution. By effectively monitoring the numerous components—including servers, networking, and storage systems—this software minimizes downtime and enhances the reliability of our applications. Ultimately, it serves as an indispensable tool for operational excellence in our innovative tech landscape.

Cerebras Systems stands out by prioritizing a culture that fosters innovation and technical excellence. In hiring for the Cluster Monitoring Software – Engineering Lead role, we look for individuals who not only have the necessary technical background but also share our passion for pushing the boundaries of AI. Our commitment to providing a supportive and inclusive work environment helps us attract top talent who are eager to contribute to our mission of revolutionizing AI technology.

For the Cluster Monitoring Software – Engineering Lead at Cerebras Systems, familiarity with tools like Prometheus and Grafana for monitoring, as well as a solid understanding of distributed systems concepts and Kubernetes, will be vital. Experience with low-level bare metal management software and networking insights can be particularly beneficial. Your ability to integrate these technologies will ensure the successful development of a comprehensive monitoring solution that meets the demands of our AI infrastructure.

When answering this question, highlight specific projects where you played a key role in monitoring distributed systems. Discuss the tools and technologies you used, any challenges you faced, and how your contributions improved system reliability and performance.

Provide examples of your previous leadership roles, focusing on your team management style, decision-making processes, and how you facilitated project success. Emphasize your ability to mentor team members and encourage collaboration.

Explain your methodology for gathering user feedback, such as conducting interviews, surveys, or usability testing. Discuss how you prioritize this feedback in your product roadmap and the positive outcomes it’s led to in past projects.

Discuss your time management skills and how you prioritize tasks effectively. Share specific techniques you've used, such as agile methodologies or sprint planning, that have helped you meet deadlines without compromising quality.

In response, outline critical metrics like system performance, resource utilization, error rates, and latency. Discuss how these metrics affect cluster management and the importance of real-time monitoring in optimizing AI application performance.

Explain the concept of observability in the context of distributed systems, focusing on the ability to measure and understand the internal states of the systems through telemetry data. Discuss how effective observability enhances incident response and system reliability.

Discuss your hands-on experience with Kubernetes, mentioning specific deployments, operations, and challenges you’ve encountered. Highlight your knowledge of tools within the Kubernetes ecosystem that facilitate monitoring and observability.

When answering, describe the context and the decision-making framework you used. Focus on how you weighed the pros and cons, involved stakeholders, and ultimately how you arrived at a solution that benefited the project and team.

Discuss your approach to redundancy, failover strategies, and regular system health checks. Emphasize the importance of automated alerts to preemptively identify issues and how proactive measures help maintain uptime in monitoring systems.

Share your passion for AI and how it drives your interest in overseeing monitoring solutions. Discuss your eagerness to create innovative tools that enhance performance and reliability of AI systems, ultimately impacting various industries positively.