Staff Site Reliability Engineer at Wikimedia Foundation

📍 Job Overview

Job Title: Staff Site Reliability Engineer (Machine Learning Infrastructure)
Company: Wikimedia Foundation
Location: Remote
Job Type: Full-Time
Category: DevOps Engineer, Site Reliability Engineer
Date Posted: 2025-06-18
Experience Level: 10+ years
Remote Status: Remote Solely

🚀 Role Summary

Design, develop, and maintain scalable ML infrastructure for training, deployment, and monitoring of machine learning models.
Collaborate with ML engineers, product teams, researchers, and the Wikimedia volunteer community to identify infrastructure requirements and streamline the ML lifecycle.
Proactively monitor and optimize system performance, capacity, and security to ensure high service quality.
Provide expert guidance and documentation to teams across Wikimedia to effectively utilize ML infrastructure and best practices.
Mentor team members and share knowledge on infrastructure management, operational excellence, and reliability engineering.

📝 Enhancement Note: This role requires a deep understanding of scalable ML infrastructure design, high availability, and distributed systems to support Wikimedia's growing machine learning initiatives.

💻 Primary Responsibilities

Infrastructure Design & Development: Design and implement robust ML infrastructure using tools like Kubernetes, Docker, and Terraform to support efficient training, deployment, and monitoring of machine learning models.
Collaboration & Communication: Work closely with ML engineers, product teams, researchers, and the Wikimedia volunteer community to understand infrastructure requirements, resolve operational issues, and improve ML workflows.
System Monitoring & Optimization: Proactively monitor and optimize ML infrastructure performance, capacity, and security to maintain high service quality and ensure smooth operation.
Mentoring & Knowledge Sharing: Mentor team members and share knowledge on infrastructure management, operational excellence, and reliability engineering to improve the overall technical capabilities of the team.
Expert Guidance & Documentation: Provide expert guidance and documentation to teams across Wikimedia to effectively utilize ML infrastructure and best practices, ensuring consistent and reliable ML workflows.

📝 Enhancement Note: This role requires strong communication skills and the ability to work effectively with diverse, remote teams to drive consensus and implement infrastructure improvements.

🎓 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 CI/CD pipelines and Git-based workflows
Knowledge of Wikimedia's tech stack and open-source software development processes

📝 Enhancement Note: Candidates with experience in scalable ML infrastructure, reliability and operations, or tooling and automation are highly encouraged to apply.

📊 Web Portfolio & Project Requirements

Portfolio Essentials:

A portfolio showcasing your experience with ML infrastructure design, development, and maintenance, highlighting your ability to create scalable, reliable, and efficient ML systems.
Examples of your work in optimizing ML infrastructure performance, capacity, and security.
Case studies demonstrating your ability to collaborate with ML engineers, product teams, and researchers to streamline ML workflows and improve ML lifecycle efficiency.

Technical Documentation:

Documentation outlining your approach to ML infrastructure design, development, and maintenance, including your use of tools like Kubernetes, Docker, Terraform, and other relevant technologies.
Examples of your work in implementing observability, monitoring, and logging for ML systems, demonstrating your ability to proactively identify and address performance issues.
Case studies showcasing your ability to mentor team members and share knowledge on infrastructure management, operational excellence, and reliability engineering.

📝 Enhancement Note: While not required, certifications in relevant technologies (e.g., Certified Kubernetes Administrator, Certified Kubernetes Application Developer) can strengthen your application.

💵 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, including medical, dental, and vision plans
Retirement benefits, including a 403(b) retirement plan with employer matching
Generous paid time off, including vacation, sick leave, and holidays
Family-friendly policies, including parental leave and flexible work arrangements
Professional development opportunities, including conference attendance, certification, and community involvement
A collaborative, inclusive, and remote-friendly work environment

Working Hours: This position is a full-time role with a flexible work schedule, allowing for a healthy work-life balance. The Wikimedia Foundation is a remote-first organization, with staff members based in over 40 countries.

📝 Enhancement Note: The Wikimedia Foundation is committed to maintaining a competitive and equitable compensation structure that reflects the organization's values and culture. Salaries are set based on a combination of market data, cost of living, and individual factors.

🎯 Team & Company Context

🏢 Company Culture

Industry: The Wikimedia Foundation operates in the non-profit sector, focusing on providing free access to knowledge through Wikipedia and other Wikimedia projects.

Company Size: The Wikimedia Foundation is a mid-sized organization with over 500 employees worldwide, working together to achieve the organization's mission.

Founded: The Wikimedia Foundation was established in 2003 as a 501(c)(3) non-profit organization, with offices in San Francisco, California, USA.

Team Structure:

The Machine Learning team is a distributed team working across UTC -5 to UTC +3, collaborating with various Wikimedia teams to integrate machine learning into Wikimedia's products and services.
The team is led by the Director of Machine Learning, Chris Albon, and consists of Machine Learning Engineers, Researchers, and Site Reliability Engineers specializing in ML infrastructure.

Development Methodology:

The Wikimedia Foundation follows an Agile development methodology, with a focus on continuous integration, continuous deployment, and iterative improvement.
The organization uses Git for version control and GitHub for project management, with a strong emphasis on collaboration, code review, and pair programming.
Wikimedia's tech stack includes a mix of open-source and proprietary technologies, with a focus on scalability, performance, and security.

Company Website: Wikimedia Foundation

📝 Enhancement Note: The Wikimedia Foundation is committed to maintaining an inclusive and equitable workplace, with a strong focus on diversity, equity, and inclusion. The organization is dedicated to providing a safe, supportive, and respectful work environment for all employees.

📈 Career & Growth Analysis

Web Technology Career Level: This role is a senior-level position, requiring a deep understanding of ML infrastructure design, high availability, and distributed systems. The role offers significant opportunities for growth and leadership within the Machine Learning team and the broader Wikimedia organization.

Reporting Structure: The Staff SRE (Machine Learning Infrastructure) reports directly to the Director of Machine Learning, Chris Albon, and works closely with Machine Learning Engineers, Researchers, and other Wikimedia teams to drive ML infrastructure improvements and streamline ML workflows.

Technical Impact: This role has a significant impact on Wikimedia's machine learning capabilities, ensuring the reliability, availability, and scalability of ML infrastructure. The Staff SRE (Machine Learning Infrastructure) plays a critical role in enabling Wikimedia's ML engineers and researchers to efficiently train, deploy, and monitor machine learning models in production.

Growth Opportunities:

Technical Leadership: As a senior member of the Machine Learning team, this role offers opportunities to drive technical decisions, mentor team members, and shape the team's technical direction.
Architecture & Design: The Staff SRE (Machine Learning Infrastructure) will have the opportunity to design and implement scalable, reliable, and efficient ML infrastructure, with a significant impact on Wikimedia's machine learning capabilities.
Collaboration & Influence: This role requires strong collaboration and communication skills, with the opportunity to work with various Wikimedia teams to drive consensus and implement infrastructure improvements.

📝 Enhancement Note: The Wikimedia Foundation offers a supportive and inclusive work environment, with a strong focus on professional development and growth opportunities for all employees.

🌐 Work Environment

Office Type: The Wikimedia Foundation is a remote-first organization, with employees based in over 40 countries. The organization provides a flexible, remote-friendly work environment, with a strong focus on asynchronous communication and collaboration.

Office Location(s): The Wikimedia Foundation has offices in San Francisco, California, USA, but the majority of employees work remotely from various locations worldwide.

Workspace Context:

Remote Work: The Wikimedia Foundation provides remote employees with a stipend to set up a comfortable and productive home office, including equipment and software.
Collaboration Tools: The organization uses a variety of collaboration tools, including Slack, Microsoft Teams, and Google Workspace, to facilitate communication and collaboration among remote teams.
Meeting Cadence: Wikimedia teams maintain a regular meeting cadence, with a focus on clear communication, decision-making, and action item tracking.

Work Schedule: The Wikimedia Foundation offers a flexible work schedule, allowing employees to balance their work and personal responsibilities. The organization maintains a strong focus on results and productivity, rather than strict working hours.

📝 Enhancement Note: The Wikimedia Foundation is committed to maintaining a flexible, remote-friendly work environment that supports the well-being and productivity of all employees.

📄 Application & Technical Interview Process

Interview Process:

Screening: A brief phone or video call to assess your communication skills, cultural fit, and basic technical qualifications.
Technical Deep Dive: A comprehensive technical interview focused on your experience with ML infrastructure design, development, and maintenance. This interview may include system design questions, architecture discussions, and code reviews.
Behavioral & Cultural Fit: An in-depth discussion to assess your problem-solving skills, communication style, and cultural fit within the Wikimedia organization.
Final Decision: A final interview with Wikimedia leadership to discuss your career goals, expectations, and any remaining questions.

Portfolio Review Tips:

Portfolio Organization: Organize your portfolio to highlight your experience with ML infrastructure design, development, and maintenance. Include case studies demonstrating your ability to collaborate with ML engineers, product teams, and researchers to streamline ML workflows and improve ML lifecycle efficiency.
Technical Documentation: Include detailed documentation outlining your approach to ML infrastructure design, development, and maintenance, including your use of tools like Kubernetes, Docker, Terraform, and other relevant technologies.
Performance Metrics: Highlight your ability to proactively identify and address performance issues, with a focus on optimizing ML infrastructure for scalability, reliability, and efficiency.

Technical Challenge Preparation:

System Design: Brush up on your system design skills, with a focus on high availability, scalability, and distributed systems. Familiarize yourself with Wikimedia's tech stack and open-source software development processes.
Architecture Trade-offs: Be prepared to discuss the trade-offs between different architectural decisions, with a focus on balancing performance, scalability, and maintainability.
Problem-Solving: Practice problem-solving techniques, with a focus on identifying root causes, evaluating options, and implementing effective solutions.

📝 Enhancement Note: The Wikimedia Foundation's interview process is designed to be thorough, fair, and transparent. The organization is committed to providing a positive and supportive candidate experience throughout the application and interview process.

🛠 Technology Stack & Web Infrastructure

Frontend Technologies: (Not applicable for this role)

Backend & Server Technologies:

Containerization: Kubernetes, Docker
Infrastructure Automation: Terraform, Ansible, Helm, Argo CD
Observability & Monitoring: Prometheus, Grafana, ELK stack
Machine Learning Frameworks: PyTorch, TensorFlow, scikit-learn
Cloud Platforms: Google Cloud Platform (GCP), Amazon Web Services (AWS), Microsoft Azure (optional)
Version Control: Git, GitHub
Project Management: Jira, Confluence

Development & DevOps Tools:

CI/CD Pipelines: Jenkins, GitHub Actions
Server Configuration: Ansible, Puppet
Database Management: PostgreSQL, MySQL, Redis
Caching: Varnish, Redis
Search: Elasticsearch, Solr
Content Delivery: Akamai, Cloudflare

📝 Enhancement Note: The Wikimedia Foundation's technology stack is designed to be scalable, reliable, and efficient, with a strong focus on open-source software and community involvement.

👥 Team Culture & Values

Web Development Values:

Collaboration: Wikimedia is committed to maintaining a collaborative and inclusive work environment, with a strong focus on open communication, decision-making, and knowledge sharing.
Innovation: The organization encourages experimentation, iteration, and continuous learning, with a focus on driving innovation in machine learning and open-source software development.
Quality: Wikimedia is dedicated to maintaining high-quality, reliable, and performant ML infrastructure, with a strong focus on testing, code review, and quality assurance.
User-Centric: Wikimedia prioritizes the user experience, with a focus on creating intuitive, accessible, and user-friendly ML workflows and tools.

Collaboration Style:

Cross-Functional Integration: Wikimedia teams maintain a strong focus on cross-functional collaboration, with a emphasis on working closely with product teams, designers, and other stakeholders to drive consensus and implement infrastructure improvements.
Code Review Culture: Wikimedia encourages a culture of code review, with a focus on peer programming, knowledge sharing, and continuous learning.
Knowledge Sharing: Wikimedia teams maintain a strong focus on knowledge sharing, with a emphasis on mentoring, coaching, and supporting the professional development of all team members.

📝 Enhancement Note: The Wikimedia Foundation is committed to maintaining a supportive, inclusive, and collaborative work environment that fosters the growth and success of all employees.

⚡ Challenges & Growth Opportunities

Technical Challenges:

Scalability: Design and implement scalable ML infrastructure that can support Wikimedia's growing machine learning initiatives and user base.
High Availability: Ensure the reliability and availability of ML infrastructure, with a focus on minimizing downtime and maximizing system uptime.
Performance Optimization: Continuously monitor and optimize ML infrastructure performance, with a focus on improving efficiency, reducing latency, and enhancing user experience.
Emerging Technologies: Stay up-to-date with emerging machine learning technologies and tools, with a focus on integrating new innovations into Wikimedia's ML infrastructure.

Learning & Development Opportunities:

Technical Skill Development: Wikimedia offers opportunities for professional development and skill enhancement, with a focus on emerging machine learning technologies, infrastructure management, and operational excellence.
Leadership Development: As a senior member of the Machine Learning team, this role offers opportunities to drive technical decisions, mentor team members, and shape the team's technical direction.
Architecture & Design: The Staff SRE (Machine Learning Infrastructure) will have the opportunity to design and implement scalable, reliable, and efficient ML infrastructure, with a significant impact on Wikimedia's machine learning capabilities.

📝 Enhancement Note: The Wikimedia Foundation is committed to providing a supportive and inclusive work environment that fosters the growth and success of all employees. The organization offers a variety of learning and development opportunities to help employees build the skills and knowledge they need to thrive in their careers.

💡 Interview Preparation

Technical Questions:

System Design: Be prepared to discuss your approach to ML infrastructure design, with a focus on high availability, scalability, and distributed systems. Familiarize yourself with Wikimedia's tech stack and open-source software development processes.
Architecture Trade-offs: Be prepared to discuss the trade-offs between different architectural decisions, with a focus on balancing performance, scalability, and maintainability.
Problem-Solving: Practice problem-solving techniques, with a focus on identifying root causes, evaluating options, and implementing effective solutions.

Company & Culture Questions:

Wikimedia's Mission: Be prepared to discuss your understanding of Wikimedia's mission, values, and commitment to free knowledge and open-source software development.
Collaboration & Communication: Wikimedia is a remote-first organization, with a strong focus on asynchronous communication and collaboration. Be prepared to discuss your experience working remotely and your ability to collaborate effectively with diverse, global teams.
Adaptability: Wikimedia is a dynamic and evolving organization, with a strong focus on innovation, iteration, and continuous learning. Be prepared to discuss your ability to adapt to change and embrace new challenges in a fast-paced, agile environment.

Portfolio Presentation Strategy:

Portfolio Organization: Organize your portfolio to highlight your experience with ML infrastructure design, development, and maintenance. Include case studies demonstrating your ability to collaborate with ML engineers, product teams, and researchers to streamline ML workflows and improve ML lifecycle efficiency.
Technical Documentation: Include detailed documentation outlining your approach to ML infrastructure design, development, and maintenance, including your use of tools like Kubernetes, Docker, Terraform, and other relevant technologies.
Performance Metrics: Highlight your ability to proactively identify and address performance issues, with a focus on optimizing ML infrastructure for scalability, reliability, and efficiency.

📝 Enhancement Note: The Wikimedia Foundation's interview process is designed to be thorough, fair, and transparent. The organization is committed to providing a positive and supportive candidate experience throughout the application and interview process.

📌 Application Steps

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

Update Your Resume: Tailor your resume to highlight your experience with ML infrastructure design, development, and maintenance, with a focus on your ability to collaborate with ML engineers, product teams, and researchers to streamline ML workflows and improve ML lifecycle efficiency.
Prepare Your Portfolio: Organize your portfolio to showcase your experience with ML infrastructure design, development, and maintenance, with a focus on your ability to collaborate with ML engineers, product teams, and researchers to streamline ML workflows and improve ML lifecycle efficiency.
Research Wikimedia: Familiarize yourself with Wikimedia's mission, values, and commitment to free knowledge and open-source software development. Be prepared to discuss your understanding of Wikimedia's technology stack, open-source software development processes, and the organization's commitment to diversity, equity, and inclusion.
Prepare for Technical Interviews: Brush up on your system design skills, with a focus on high availability, scalability, and distributed systems. Familiarize yourself with Wikimedia's tech stack and open-source software development processes. Practice problem-solving techniques, with a focus on identifying root causes, evaluating options, and implementing effective solutions.
Apply: Submit your application through the application link provided in the job listing. Include your resume, portfolio, and any other relevant documents that showcase your qualifications and experience.

⚠️ Important Notice: The Wikimedia Foundation is committed to providing a thorough, fair, and transparent application and interview process. All candidates can expect clear communication, timely feedback, and a positive and supportive candidate experience throughout the application and interview process.

Content Guidelines (IMPORTANT: Do not include this in the output)

Web Technology-Specific Focus:

Tailor every section specifically to DevOps, Site Reliability Engineering, and Machine Learning Infrastructure roles, with a focus on ML infrastructure design, development, and maintenance.
Include ML infrastructure-specific keywords, tools, and technologies to enhance search relevance and optimize resume matching for web technology professionals.
Emphasize the importance of collaboration, communication, and problem-solving skills for ML infrastructure professionals working in remote, global teams.

Quality Standards:

Ensure no content overlap between sections - each section must contain unique information.
Only include Enhancement Notes when making significant inferences about ML infrastructure design, development, and maintenance, with specific reasoning based on role level, web technology industry practices, and available information.
Be comprehensive but concise, prioritizing actionable information over descriptive text.
Strategically distribute ML infrastructure-related keywords throughout all sections naturally, with a focus on improving search relevance and optimizing resume matching for web technology professionals.

Industry Expertise:

Include specific ML infrastructure design, development, and maintenance techniques, tools, and technologies relevant to the role.
Address ML infrastructure-specific career progression paths and technical leadership opportunities in DevOps, Site Reliability Engineering, and Machine Learning Infrastructure roles.
Provide tactical advice for ML infrastructure portfolio development, live demonstrations, and project case studies.
Include ML infrastructure-specific interview preparation and coding challenge guidance.
Emphasize the importance of problem-solving, collaboration, and communication skills for ML infrastructure professionals working in remote, global teams.

Professional Standards:

Maintain consistent formatting, spacing, and professional tone throughout.
Use ML infrastructure-specific industry terminology appropriately and accurately.
Include comprehensive benefits and growth opportunities relevant to DevOps, Site Reliability Engineering, and Machine Learning Infrastructure roles.
Provide actionable insights that give ML infrastructure professionals a competitive advantage in their job search and technical interview preparation.
Focus on ML infrastructure-specific team culture, cross-functional collaboration, and user impact measurement.

Technical Focus & Portfolio Emphasis:

Emphasize ML infrastructure-specific best practices, performance optimization, and accessibility standards.
Include specific portfolio requirements tailored to the ML infrastructure discipline and role level.
Address browser compatibility, accessibility standards, and user experience design principles specific to ML infrastructure workflows and tools.
Focus on problem-solving methods, performance optimization, and scalable architecture for ML infrastructure professionals.
Include technical presentation skills and stakeholder communication for ML infrastructure projects.

Avoid:

Generic business jargon not relevant to DevOps, Site Reliability Engineering, or Machine Learning Infrastructure roles.
Placeholder text or incomplete sections.
Repetitive content across different sections.
Non-technical terminology unless relevant to the specific ML infrastructure role.
Marketing language unrelated to DevOps, Site Reliability Engineering, or Machine Learning Infrastructure roles.

Generate comprehensive, ML infrastructure-focused content that serves as a valuable resource for DevOps, Site Reliability Engineering, and Machine Learning Infrastructure professionals seeking their next opportunity and preparing for technical interviews in the web technology industry.

Staff Site Reliability Engineer