Staff Site Reliability Engineer

At the Wikimedia Foundation, a Staff Site Reliability Engineer (SRE) is primarily responsible for designing, developing, maintaining, and scaling our ML infrastructure. This includes implementing systems for machine learning model training, deployment, and monitoring, collaborating closely with other teams to enhance system reliability and performance, and providing documentation and guidance for best practices in ML infrastructure usage.

To qualify for the Staff Site Reliability Engineer position at the Wikimedia Foundation, candidates should ideally have over 7 years of experience in SRE, DevOps, or infrastructure engineering. A strong background in managing production-grade machine learning systems, as well as proficiency with tools like Kubernetes, Docker, and infrastructure automation tools, is essential. Familiarity with Python-based ML frameworks to support development initiatives is also preferred.

The Wikimedia Foundation ensures that Staff Site Reliability Engineers are supported through mentorship opportunities, a collaborative work environment, and resources for continuous learning. Our culture promotes knowledge sharing and includes expert guidance on best practices for infrastructure management, enhancing the overall operational excellence and reliability in our workflows.

As a Staff Site Reliability Engineer at the Wikimedia Foundation, you will work on a variety of projects aimed at building and optimizing machine learning infrastructure. This could involve implementing robust monitoring systems, ensuring high availability and security of ML workloads, and collaborating with cross-functional teams to solve operational challenges. Your contributions will directly support our mission to share knowledge freely and broadly.

The work culture for Staff Site Reliability Engineers at the Wikimedia Foundation is highly collaborative, inclusive, and remote-friendly. We value diverse perspectives, encourage proactive involvement, and promote systems thinking and operational excellence. Being part of a global team means you'll interact with talented professionals from various backgrounds, contributing to an enriching workplace experience.

When responding to this question, highlight specific projects you’ve managed, focusing on the systems and technologies you used, such as Kubernetes or distributed training frameworks. Emphasize how your contributions led to improvements in reliability and scalability of ML workflows.

Discuss your methodology for performance monitoring, including tools like Prometheus or Grafana that you’ve employed. Explain your processes for identifying bottlenecks, implementing fixes, and continually optimizing systems for better performance.

Describe your experience with infrastructure automation tools like Terraform, Ansible, or Helm. Include examples where you’ve successfully implemented automation to streamline processes, reduce errors, or enhance deployment speeds.

When answering this, detail specific strategies that you’ve used to maintain high availability, such as redundancy, load balancing, or multi-region deployments. Share experiences where your efforts resulted in improved system uptime.

Explain how collaboration is integral to your role, providing examples of successful partnerships with ML engineers or researchers. Discuss how you share knowledge, resolve issues, and foster a positive working relationship among team members.

Share a specific challenge you faced, the steps you took to analyze and solve it, and the positive outcome that resulted. Focus on showcasing your analytical skills and how you tackled complex problems under pressure.

Discuss important metrics like model performance, system latency, uptime, and resource utilization. Share how you’ve utilized these metrics in your past roles to improve the reliability and efficiency of ML systems.

Explain your mentoring style and the importance of knowledge transfer. Share specific instances where you’ve provided guidance and support to junior team members, helping them grow in their roles.

Mention the resources you use to stay informed, such as blogs, online courses, or community forums. Highlight your commitment to continuous learning and adapting new technologies to enhance your practice as an SRE.

Articulate your passion for open source and your alignment with the Wikimedia Foundation’s mission. Discuss how your skills and values resonate with their goals and your eagerness to contribute to a nonprofit that makes knowledge freely available.