Distributed Software Engineer

As a Distributed Software Engineer at Cerebras Systems, your responsibilities will primarily revolve around automating bare-metal configuration of networking, operating systems, and application software for large clusters of our Wafer-Scale Engine systems. You'll also be involved in developing workflows for cluster upgrades and security patching, managing resource allocation and job submission within a multi-user environment, and enhancing monitoring systems to ensure high availability and performance. Your role will be crucial in maintaining the operational efficiency and reliability of our powerful AI compute clusters.

To qualify for the Distributed Software Engineer position at Cerebras Systems, candidates should possess a robust background in software architecture and development for at least 6 years. A strong understanding of distributed cluster environments and expertise in Kubernetes (K8s), Prometheus, and Grafana is essential. Proficiency in programming languages like GoLang and Python, alongside strong debugging skills and experience in writing tests for new features, are critical for success in this role.

Cerebras Systems fosters a supportive and inclusive work environment that encourages continuous learning and individual growth. As a new Distributed Software Engineer, you will have the opportunity to collaborate with experienced professionals while contributing to groundbreaking AI technologies. The non-corporate culture respects individual beliefs, allowing for personal expression and innovation, making it an ideal place for those eager to evolve in their career.

Candidates for the Distributed Software Engineer role at Cerebras Systems should have a deep understanding of distributed systems architecture and be familiar with technologies and tools such as Kubernetes, Prometheus, and Grafana. Proficiency in programming languages like GoLang, Python, and bash is vital, as these are used in developing software solutions tailored for our AI compute clusters. Additionally, experience with debugging and testing in distributed environments will greatly enhance your capability to succeed in this position.

The role of Distributed Software Engineer at Cerebras Systems is unique due to our pioneering work with wafer-scale architecture that has transformed the AI landscape. Unlike conventional roles focused solely on software, this position involves creating innovative solutions for one of the largest AI supercomputers in the world. You’ll work at the intersection of hardware and software, all while contributing to an environment that values creativity and technical excellence.

Discuss your past projects involving distributed systems, focusing on your specific roles and contributions. Highlight challenges faced and how you optimized the system’s performance and reliability. Use examples to show your understanding of scaling, monitoring, and debugging distributed applications.

Share specific instances where you implemented Kubernetes in your projects. Discuss how you managed container orchestration, handled deployments, and ensured high availability. Providing metrics on how Kubernetes improved system performance will strengthen your response.

Explain your methodology for automating workflows, particularly steps you’ve taken in past positions. Discuss tools and scripting languages you used, and how automation improved efficiency and reduced downtime. Emphasize the importance of documentation and testing in your automation strategies.

Discuss the importance of monitoring tools like Prometheus and Grafana, and how you have utilized them in previous distributed system projects. Highlight specific metrics you monitored and how they helped in proactive problem resolution and performance tuning.

Talk about security best practices you’ve implemented in past roles, including regular security patches, vulnerability assessments, and compliance with security standards. Share examples of specific security measures tailored for clusters that you’ve executed successfully.

Highlight your approach to debugging, emphasizing systematic analysis and using diagnostic tools. If applicable, share a challenging debugging scenario you've faced, what steps you took, and the outcome of your efforts in resolving the issue.

Discuss the tools and methodologies you have utilized for job scheduling and resource allocation. Explain how you prioritize tasks, manage dependencies, and ensure efficient resource use while maintaining performance across multiple users.

Share your perspective on automation's vital role in increasing efficiency, accuracy, and scalability in managing clusters. Provide examples where automation has improved your workflow, reduced errors, and enhanced overall system reliability.

Explain your approach to professional development, such as attending conferences, participating in webinars, or following key industry blogs and publications. Mention specific technologies you’re currently exploring or applying relevant trends in your work.

Highlight collaboration experiences in your past roles, focusing on tools for communication and project management. Discuss how you’ve navigated differing opinions, facilitated discussions, and contributed to achieving team goals in a distributed systems context.