📍 Job Overview

Job Title: Staff Site Reliability Engineer (Machine Learning Infrastructure)
Company: Wikimedia Foundation
Location: Remote
Job Type: Full-Time
Category: DevOps, Infrastructure
Date Posted: June 18, 2025
Experience Level: 10+ years
Remote Status: Remote Solely

🚀 Role Summary

Key Responsibilities: Design, develop, and maintain scalable ML infrastructure for training, deployment, and monitoring of machine learning models. Collaborate with ML engineers, researchers, and SRE teams to ensure high service quality and streamline ML workflows.
Key Skills: Site Reliability Engineering (SRE), DevOps, Infrastructure Engineering, Machine Learning Systems, Kubernetes, Docker, GPU Acceleration, Distributed Training Systems, Infrastructure Automation, Configuration Management, Observability, Monitoring, Logging, Python, Machine Learning Frameworks, Collaboration, Documentation.

📝 Enhancement Note: This role requires a strong background in SRE, DevOps, or infrastructure engineering with a focus on machine learning systems. Proficiency in infrastructure automation tools and observability for ML systems is essential.

💻 Primary Responsibilities

Infrastructure Design & Development: Design and implement robust ML infrastructure for training, deployment, monitoring, and scaling of machine learning models.
Collaboration & Problem-Solving: Collaborate with ML engineers, product teams, researchers, SREs, and the Wikimedia volunteer community to identify infrastructure requirements, resolve operational issues, and streamline the ML lifecycle.
System Performance & Optimization: Proactively monitor and optimize system performance, capacity, and security to maintain high service quality.
Mentoring & Knowledge Sharing: Mentor team members and share knowledge on infrastructure management, operational excellence, and reliability engineering.
Expert Guidance & Documentation: Provide expert guidance and documentation to teams across Wikimedia to effectively utilize the ML infrastructure and best practices.

📝 Enhancement Note: This role requires a deep understanding of scalable infrastructure design for high-performance machine learning training and inference workloads, as well as expertise in ensuring the high reliability and robust operations of complex, distributed ML systems at scale.

🎓 Skills & Qualifications

Education: A Bachelor's degree in Computer Science, Engineering, or a related field. Relevant experience may be considered in lieu of a degree.

Experience: 7+ years of experience in Site Reliability Engineering (SRE), DevOps, or infrastructure engineering roles, with substantial exposure to production-grade machine learning systems.

Required Skills:

Proficiency 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

Preferred Skills:

Experience with cloud-based ML infrastructure (e.g., GCP, AWS, Azure)
Familiarity with MLOps best practices and workflows
Knowledge of Wikimedia's tech stack and open-source software development processes

📝 Enhancement Note: Candidates with expertise in scalable ML infrastructure, reliability and operations, or tooling and automation are highly desired.

📊 Web Portfolio & Project Requirements

Portfolio Essentials:

A portfolio showcasing your experience with machine learning infrastructure, including examples of system design, deployment, and monitoring.
Documentation demonstrating your understanding of best practices for ML infrastructure management and operational excellence.
Examples of your ability to collaborate with diverse teams and stakeholders to deliver high-quality ML infrastructure solutions.

Technical Documentation:

Detailed system design documents (SDDs) outlining your approach to ML infrastructure challenges and solutions.
Code comments and documentation demonstrating your commitment to code quality and maintainability.
Performance metrics and optimization techniques used to ensure the scalability and reliability of your ML infrastructure projects.

📝 Enhancement Note: Given the remote nature of this role, a well-curated portfolio and strong communication skills are crucial for success.

💵 Compensation & Benefits

Salary Range: The anticipated annual pay range for this position 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.

Benefits:

Comprehensive health coverage (medical, dental, and vision) for employees and their eligible dependents
Paid time off (vacation, sick leave, and holidays)
Retirement benefits and employee stock purchase plan
Professional development opportunities and tuition reimbursement
Employee assistance program and wellness resources
Paid parental leave and family care leave

Working Hours: This role requires a standard 40-hour workweek with flexibility for deployment windows, maintenance, and project deadlines.

📝 Enhancement Note: Wikimedia Foundation's compensation and benefits package is designed to be competitive, equitable, and consistent with their values and culture.

🎯 Team & Company Context

🏢 Company Culture

Industry: Wikimedia Foundation is a non-profit organization that operates Wikipedia and other Wikimedia free knowledge projects. Their primary mission is to bring free knowledge to every person in the world.

Company Size: Wikimedia Foundation has a distributed team working across UTC -5 to UTC +3 (Eastern Americas, Europe, and Africa), with a focus on open-source software development and volunteer communities.

Founded: Wikimedia Foundation was launched in 2003, with a commitment to providing free access to the sum of all human knowledge.

Team Structure:

The Machine Learning team consists of Engineers, Researchers, and Data Scientists working on various projects to improve Wikimedia's platforms and services.
The Site Reliability Engineering (SRE) team is responsible for ensuring the reliability, availability, and scalability of Wikimedia's infrastructure.
The Wikimedia Foundation values a collaborative, proactive, and independently motivated work environment, with a strong commitment to open-source software and volunteer communities.

Development Methodology:

Wikimedia Foundation follows Agile methodologies, with a focus on continuous integration, continuous deployment, and iterative development.
The organization emphasizes code review, testing, and quality assurance practices to ensure high-quality software products.
Wikimedia Foundation uses deployment strategies, CI/CD pipelines, and server management tools to automate and streamline the software development process.

Company Website: Wikimedia Foundation

📝 Enhancement Note: Wikimedia Foundation's commitment to open-source software and volunteer communities is a significant aspect of their company culture and work environment.

📈 Career & Growth Analysis

Web Technology Career Level: This Staff Site Reliability Engineer (Machine Learning Infrastructure) role is an advanced, senior-level position that requires a deep understanding of scalable infrastructure design, machine learning systems, and operational excellence.

Reporting Structure: This role reports directly to the Director of Machine Learning, Chris Albon, and collaborates closely with ML engineers, product teams, researchers, SREs, and the Wikimedia volunteer community.

Technical Impact: The primary responsibility of this role is to design, develop, maintain, and scale the foundational infrastructure that enables Wikimedia's Machine Learning Engineers and Researchers to efficiently train, deploy, and monitor machine learning models in production. This has a significant impact on Wikimedia's ability to provide high-quality, relevant, and up-to-date information to its users.

Growth Opportunities:

Technical Growth: Deepen expertise in machine learning infrastructure, scalable system design, and operational excellence.
Leadership Development: Mentor team members and contribute to the development of Wikimedia's SRE and Machine Learning teams.
Architecture Decisions: Influence the direction of Wikimedia's ML infrastructure and contribute to strategic architecture decisions.

📝 Enhancement Note: Wikimedia Foundation offers significant growth opportunities for technical professionals looking to advance their careers in machine learning infrastructure and site reliability engineering.

🌐 Work Environment

Office Type: Wikimedia Foundation is a remote-first organization with staff members based in over 40 countries. The Machine Learning team works asynchronously across UTC -5 to UTC +3 (Eastern Americas, Europe, and Africa).

Office Location(s): Wikimedia Foundation has offices in San Francisco, California, USA, but the majority of its staff works remotely.

Workspace Context:

Wikimedia Foundation's remote work environment emphasizes collaboration, communication, and self-motivation.
The organization provides the necessary tools and resources for remote work, including multiple monitors, testing devices, and development tools.
Wikimedia Foundation encourages a culture of knowledge sharing, technical mentoring, and continuous learning.

Work Schedule: Wikimedia Foundation offers a flexible work schedule with core hours and regular team meetings to ensure collaboration and productivity. The organization values work-life balance and provides resources to support employee well-being.

📝 Enhancement Note: Wikimedia Foundation's remote work environment requires strong communication skills, self-motivation, and the ability to work effectively in a global, asynchronous team.

📄 Application & Technical Interview Process

Interview Process:

Phone Screen: A brief phone call to discuss your background, experience, and motivation for the role.
Technical Deep Dive: A comprehensive technical interview focused on your expertise in machine learning infrastructure, system design, and problem-solving.
Behavioral & Cultural Fit: An interview to assess your cultural fit, communication skills, and alignment with Wikimedia Foundation's values and mission.
Final Decision: A final interview with the Director of Machine Learning, Chris Albon, to discuss your fit for the role and the organization.

Portfolio Review Tips:

Highlight your experience with machine learning infrastructure, system design, and deployment.
Include examples of your ability to collaborate with diverse teams and stakeholders to deliver high-quality ML infrastructure solutions.
Demonstrate your understanding of best practices for ML infrastructure management and operational excellence.

Technical Challenge Preparation:

Brush up on your knowledge of machine learning frameworks, infrastructure automation tools, and observability for ML systems.
Practice system design exercises and prepare for questions on scalable infrastructure design, machine learning systems, and operational excellence.
Familiarize yourself with Wikimedia Foundation's tech stack and open-source software development processes.

ATS Keywords: [List of relevant web development, server administration, and machine learning infrastructure keywords for resume optimization]

📝 Enhancement Note: Wikimedia Foundation's interview process is designed to assess your technical expertise, cultural fit, and alignment with the organization's mission and values.

🛠 Technology Stack & Web Infrastructure

Frontend Technologies: N/A (This role focuses on machine learning infrastructure and does not involve frontend development)

Backend & Server Technologies:

Kubernetes: For container orchestration and deployment of machine learning workloads
Docker: For creating, deploying, and running machine learning applications
GPU Acceleration: For efficient training and inference of machine learning models
Distributed Training Systems: For parallel and distributed processing of machine learning workloads

Development & DevOps Tools:

Terraform: For infrastructure as code (IaC) and automated deployment of machine learning infrastructure
Ansible: For configuration management and automation of machine learning workloads
Helm: For package management and deployment of machine learning applications on Kubernetes
Argo CD: For continuous deployment and automated updates of machine learning infrastructure
Prometheus: For monitoring and alerting of machine learning infrastructure performance
Grafana: For visualization and analysis of machine learning infrastructure metrics

📝 Enhancement Note: Wikimedia Foundation's technology stack is designed to support the efficient training, deployment, and monitoring of machine learning models in production.

👥 Team Culture & Values

Web Development Values:

Collaboration: Wikimedia Foundation emphasizes collaboration, knowledge sharing, and technical mentoring to ensure high-quality ML infrastructure solutions.
Proactivity: The organization values proactive problem-solving, innovation, and a commitment to continuous improvement.
Expertise: Wikimedia Foundation expects its team members to have deep expertise in their respective fields and a commitment to staying up-to-date with industry trends and best practices.
Innovation: The organization encourages experimentation, iteration, and a willingness to challenge the status quo in pursuit of better ML infrastructure solutions.

Collaboration Style:

Wikimedia Foundation fosters a culture of cross-functional integration between ML engineers, researchers, product teams, SREs, and the Wikimedia volunteer community.
The organization emphasizes code review, peer programming, and knowledge sharing to ensure high-quality ML infrastructure solutions.
Wikimedia Foundation values a culture of continuous learning, technical mentoring, and community involvement.

📝 Enhancement Note: Wikimedia Foundation's team culture is built on a foundation of collaboration, proactivity, expertise, innovation, and a commitment to open-source software and volunteer communities.

⚡ Challenges & Growth Opportunities

Technical Challenges:

Scalability: Design and implement scalable ML infrastructure solutions that can handle Wikimedia's growing user base and data requirements.
Reliability: Ensure high availability and fault tolerance of ML infrastructure to minimize downtime and maintain user trust.
Performance Optimization: Continuously monitor and optimize ML infrastructure performance to maximize efficiency and cost-effectiveness.
Security: Implement robust security measures to protect Wikimedia's ML infrastructure and user data from unauthorized access and malicious attacks.

Learning & Development Opportunities:

Technical Skill Development: Deepen your expertise in machine learning infrastructure, scalable system design, and operational excellence through workshops, conferences, and online learning resources.
Conference Attendance & Certification: Wikimedia Foundation encourages attendance at relevant conferences and certifications to stay current with industry trends and best practices.
Technical Mentorship & Leadership Development: Mentor team members and contribute to the development of Wikimedia's SRE and Machine Learning teams to advance your leadership skills and technical expertise.

📝 Enhancement Note: Wikimedia Foundation offers significant technical challenges and growth opportunities for candidates looking to advance their careers in machine learning infrastructure and site reliability engineering.

💡 Interview Preparation

Technical Questions:

System Design: Describe your approach to designing scalable, reliable, and efficient machine learning infrastructure for Wikimedia's production environment.
Problem-Solving: Walkthrough a complex ML infrastructure challenge you've faced in the past and how you approached solving it.
Observability & Monitoring: Explain your strategies for monitoring and alerting on ML infrastructure performance, and how you ensure high service quality.

Company & Culture Questions:

Wikimedia Foundation Mission: Explain why you're passionate about Wikimedia Foundation's mission to bring free knowledge to every person in the world.
Open-Source Software: Describe your experience with open-source software development and how you've contributed to the broader community.
Volunteer Communities: Share your thoughts on the importance of volunteer communities in Wikimedia Foundation's success and how you've engaged with them in the past.

Portfolio Presentation Strategy:

ML Infrastructure Portfolio: Highlight your experience with machine learning infrastructure, system design, and deployment, including examples of your ability to collaborate with diverse teams and stakeholders.
Technical Documentation: Demonstrate your understanding of best practices for ML infrastructure management and operational excellence through detailed system design documents, code comments, and performance metrics.
User Experience Impact: Explain how your ML infrastructure solutions have improved user experience, accessibility, and the overall quality of Wikimedia's platforms and services.

📝 Enhancement Note: Wikimedia Foundation's interview process is designed to assess your technical expertise, cultural fit, and alignment with the organization's mission and values, as well as your ability to collaborate with diverse teams and stakeholders to deliver high-quality ML infrastructure solutions.

📌 Application Steps

To apply for this Staff Site Reliability Engineer (Machine Learning Infrastructure) position at Wikimedia Foundation:

Customize Your Portfolio: Tailor your portfolio to showcase your experience with machine learning infrastructure, system design, and deployment, highlighting your ability to collaborate with diverse teams and stakeholders.
Optimize Your Resume: Highlight your relevant technical skills, experience, and accomplishments, with a focus on machine learning infrastructure, system design, and problem-solving.
Prepare for Technical Interviews: Brush up on your knowledge of machine learning frameworks, infrastructure automation tools, and observability for ML systems. Practice system design exercises and prepare for questions on scalable infrastructure design, machine learning systems, and operational excellence.
Research Wikimedia Foundation: Familiarize yourself with Wikimedia Foundation's mission, values, and technology stack. Understand how your ML infrastructure expertise can contribute to the organization's success and make a positive impact on its users.

⚠️ Important Notice: This enhanced job description includes AI-generated insights and web development/server administration industry-standard assumptions. All details should be verified directly with the hiring organization before making application decisions.

Staff Site Reliability Engineer