Staff Site Reliability Engineer

Summary

Join the Wikimedia Foundation as a Staff Site Reliability Engineer (SRE) specializing in Machine Learning Infrastructure! You will be part of a global team, reporting to the Director of Machine Learning. Your main responsibility is designing, developing, maintaining, and scaling the infrastructure for Wikimedia's ML engineers and researchers. You will collaborate with various teams, monitor system performance, and mentor team members. This role requires extensive experience in SRE, DevOps, and ML infrastructure. The Wikimedia Foundation is a remote-first organization offering competitive salaries and benefits.

Requirements

• 7+ years of experience in Site Reliability Engineering (SRE), DevOps, or infrastructure engineering roles, with substantial exposure to production-grade machine learning systems
• Proven expertise with on-premises infrastructure for machine learning workloads (e.g., Kubernetes, Docker, GPU acceleration, distributed training systems)
• Strong proficiency with infrastructure automation and configuration management tools (e.g., Terraform, Ansible, Helm, Argo CD)
• Experience implementing observability, monitoring, and logging for ML systems (e.g., Prometheus, Grafana, ELK stack)
• Familiarity with popular Python-based ML frameworks (e.g., PyTorch, TensorFlow, scikit-learn)
• Strong English communication skills and comfort working asynchronously across global teams

Responsibilities

• Design and implement robust ML infrastructure used for training, deployment, monitoring, and scaling of machine learning models
• Improve reliability, availability, and scalability of ML infrastructure, ensuring smooth and efficient workflows for internal ML engineers and researchers
• Collaborate closely with ML engineers, product teams, researchers, SREs, and the Wikimedia volunteer community to identify infrastructure requirements, resolve operational issues, and streamline the ML lifecycle
• Proactively monitor and optimize system performance, capacity, and security to maintain high service quality
• Provide expert guidance and documentation to teams across Wikimedia to effectively utilize the ML infrastructure and best practices
• Mentor team members and share knowledge on infrastructure management, operational excellence, and reliability engineering

Preferred Qualifications

• Scalable ML Infrastructure: Deep understanding of scalable infrastructure design for high-performance machine learning training and inference workloads
• Reliability and Operations: Proven track record ensuring high reliability and robust operations of complex, distributed ML systems at scale
• Tooling and Automation: Demonstrated expertise creating robust tooling and automation solutions that simplify the deployment, management, and monitoring of ML infrastructure

Benefits

• The anticipated annual pay range of this position for applicants based within the United States is US$ 129,347 to US$ 200,824 with multiple individualized factors, including cost of living in the location, being the determinants of the offered pay
• For applicants located outside of the US, the pay range will be adjusted to the country of hire
• We neither ask for nor take into consideration the salary history of applicants
• The Wikimedia Foundation is a remote-first organization