Staff Site Reliability Engineer

Summary

Join the Wikimedia Foundation as a Staff Site Reliability Engineer (SRE) specializing in Machine Learning Infrastructure and contribute to the design, development, maintenance, and scaling of the infrastructure that powers Wikimedia's machine learning initiatives. You will collaborate with a global team, working across multiple time zones, to ensure the reliability, availability, and scalability of ML infrastructure. Responsibilities include designing robust ML infrastructure, improving system performance and security, and providing expert guidance to internal teams. The ideal candidate possesses extensive experience in SRE, DevOps, or infrastructure engineering, with a strong background in machine learning systems and infrastructure automation. This role requires proficiency in various tools and technologies, including Kubernetes, Docker, Terraform, and Python-based ML frameworks. The Wikimedia Foundation offers a competitive salary and benefits package.

Requirements

• Be based within UTC -5 to UTC +3 time zones to ensure good collaboration overlap with the team
• 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

• Collaborative, proactive, and independently motivated
• Experienced working with diverse, remote teams
• Committed to open-source software and volunteer communities
• Systematic thinker focused on operational excellence and reliability
• Deep understanding of scalable infrastructure design for high-performance machine learning training and inference workloads
• Proven track record ensuring high reliability and robust operations of complex, distributed ML systems at scale
• Demonstrated expertise creating robust tooling and automation solutions that simplify the deployment, management, and monitoring of ML infrastructure

Benefits

• Salaries at the Wikimedia Foundation are set in a way that is competitive, equitable, and consistent with our values and culture
• 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