Staff Site Reliability Engineer

Wikimedia Foundation; United States; Remote; USD 90,000 - 150,000; Full time

Job summary

An established industry player is seeking a Staff Site Reliability Engineer focused on Machine Learning Infrastructure. In this pivotal role, you will design and maintain the foundational infrastructure that empowers machine learning engineers and researchers. Your expertise will enhance the reliability, availability, and scalability of ML systems, ensuring seamless workflows. Collaborating with diverse teams, you will tackle operational challenges and mentor others in best practices. This is a unique opportunity to contribute to a mission-driven organization that values knowledge sharing and inclusivity while working in a dynamic, remote environment.

Benefits

Flexible working hours

Remote work options

Professional development opportunities

Health care benefits

Generous vacation policy

Collaborative work environment

Qualifications

• 7+ years in SRE, DevOps, or infrastructure roles with ML systems experience.
• Expertise in Kubernetes, Docker, and infrastructure automation tools.

Responsibilities

• Design and implement ML infrastructure for training and deployment.
• Collaborate with teams to optimize ML lifecycle and system performance.

Skills

Site Reliability Engineering (SRE)

DevOps

Machine Learning Systems

Kubernetes

Docker

Infrastructure Automation

Terraform

Ansible

Prometheus

Python

Education

Bachelor's Degree in Computer Science or related field

Tools

Grafana

ELK stack

Helm

Argo CD

Job description

The Wikimedia Foundation is looking for a Staff Site Reliability Engineer (SRE) focused on Machine Learning Infrastructure. You will join a distributed team working across UTC -5 to UTC +3 (Eastern Americas, Europe, and Africa) and report directly to the Director of Machine Learning, Chris Albon.

As a Staff SRE specializing in ML infrastructure, your primary responsibility is designing, developing, maintaining, and scaling the foundational infrastructure that enables Wikimedia's Machine Learning Engineers and Researchers to efficiently train, deploy, and monitor machine learning models in production.

You will be responsible for:

• 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.

Skills and Experience:

• 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.

Qualities that are important to us:

• 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.

Additionally, ideal candidates will excel in at least one of these areas:

• 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.

About the Wikimedia Foundation

The Wikimedia Foundation is the nonprofit organization that operates Wikipedia and the other Wikimedia free knowledge projects. Our vision is a world in which every single human can freely share in the sum of all knowledge. We believe that everyone has the potential to contribute something to our shared knowledge, and that everyone should be able to access that knowledge freely. We host Wikipedia and the Wikimedia projects, build software experiences for reading, contributing, and sharing Wikimedia content, support the volunteer communities and partners who make Wikimedia possible, and advocate for policies that enable Wikimedia and free knowledge to thrive.

The Wikimedia Foundation is a charitable, not-for-profit organization that relies on donations. We receive donations from millions of individuals around the world, with an average donation of about $15. We also receive donations through institutional grants and gifts. The Wikimedia Foundation is a United States 501(c)(3) tax-exempt organization with offices in San Francisco, California, USA.

As an equal opportunity employer, the Wikimedia Foundation values having a diverse workforce and continuously strives to maintain an inclusive and equitable workplace. We encourage people with a diverse range of backgrounds to apply. We do not discriminate against any person based upon their race, traits historically associated with race, religion, color, national origin, sex, pregnancy or related medical conditions, parental status, sexual orientation, gender identity, gender expression, age, status as a protected veteran, status as an individual with a disability, genetic information, or any other legally protected characteristics.