Full-Time Staff Site Reliability Engineer

The Wikimedia Foundation seeks a Staff Site Reliability Engineer (SRE) focused on ML Infrastructure.

You'll join a distributed team (UTC -5 to UTC +3) and report to the Director of Machine Learning.

Responsibilities:

• Design, develop, maintain, and scale foundational ML infrastructure for ML Engineers & Researchers.
• Improve reliability, availability, and scalability of ML infrastructure.
• Collaborate with ML engineers, product teams, researchers, SREs, and the Wikimedia volunteer community.
• Proactively monitor and optimize system performance, capacity, and security.
• Provide guidance and documentation on using the ML infrastructure.
• Mentor team members on infrastructure management and reliability engineering.

Skills & Experience:

• 7+ years of SRE/DevOps/Infrastructure Engineering experience with production-grade ML systems.
• Expertise with on-premises ML infrastructure (Kubernetes, Docker, GPU acceleration, distributed training systems).
• Proficiency with infrastructure automation and configuration management tools (Terraform, Ansible, Helm, Argo CD).
• Experience implementing observability, monitoring, and logging for ML systems (Prometheus, Grafana, ELK stack).
• Familiarity with Python-based ML frameworks (PyTorch, TensorFlow, scikit-learn).
• Strong English communication skills for global team collaboration.

Qualities:

• Collaborative, proactive, and independently motivated.
• Experienced with diverse, remote teams.
• Committed to open-source software and volunteer communities.
• Systematic thinker focused on operational excellence.

Ideal Candidates Excel in:

• Scalable ML Infrastructure: Deep understanding of scalable infrastructure design for ML training/inference.
• Reliability and Operations: Proven track record ensuring reliability of complex, distributed ML systems.
• Tooling and Automation: Expertise creating robust tooling/automation for ML infrastructure.

What is the last date for applying to the job?

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