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, 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 and efficiency of machine learning models. Responsibilities include designing robust ML infrastructure, improving system performance and scalability, 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 popular Python-based ML frameworks. The Wikimedia Foundation offers a competitive salary and benefits package.

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

• Designing and implementing robust ML infrastructure used for training, deployment, monitoring, and scaling of machine learning models
• Improving reliability, availability, and scalability of ML infrastructure, ensuring smooth and efficient workflows for internal ML engineers and researchers
• Collaborating 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 monitoring and optimizing system performance, capacity, and security to maintain high service quality
• Providing expert guidance and documentation to teams across Wikimedia to effectively utilize the ML infrastructure and best practices
• Mentoring team members and sharing 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
• 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