📍 Job Overview

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

🚀 Role Summary

Design, develop, and maintain scalable machine learning infrastructure for training, deployment, and monitoring of ML models.
Collaborate with ML engineers, researchers, and SRE teams to ensure high reliability, availability, and scalability of ML systems.
Provide expert guidance and mentorship to teams across Wikimedia on ML infrastructure best practices.
Proactively monitor and optimize system performance, capacity, and security to maintain high service quality.

📝 Enhancement Note: This role requires a strong background in Site Reliability Engineering (SRE) and DevOps, with a focus on machine learning infrastructure. Experience with on-premises infrastructure and automation tools is crucial for success in this position.

💻 Primary Responsibilities

Infrastructure Design & Development: Design, implement, and maintain robust ML infrastructure for training, deployment, monitoring, and scaling of machine learning models.
Collaboration & Stakeholder Management: Work closely with ML engineers, researchers, product teams, 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 ensure high service quality and reliability.
Mentoring & Knowledge Sharing: 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.
Emerging Technologies & Trends: Stay up-to-date with emerging machine learning technologies and trends, and incorporate them into Wikimedia's ML infrastructure as appropriate.

📝 Enhancement Note: This role requires a deep understanding of scalable infrastructure design for high-performance machine learning training and inference workloads, as well as a proven track record ensuring 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:

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.

Preferred Skills:

Experience with cloud-based ML infrastructure (e.g., GCP, AWS, Azure) and hybrid/multi-cloud environments.
Knowledge of CI/CD pipelines and automated deployment strategies for ML models.
Familiarity with data processing and feature store technologies (e.g., Apache Beam, Apache Arrow, Temporal).
Experience with MLOps platforms and workflow orchestration tools (e.g., Kubeflow, MLflow, TensorFlow Extended).

📝 Enhancement Note: While not required, experience with cloud-based ML infrastructure and hybrid/multi-cloud environments can be beneficial for this role, as Wikimedia's ML infrastructure is evolving to incorporate cloud-based solutions alongside its on-premises components.

📊 Web Portfolio & Project Requirements

Portfolio Essentials:

A portfolio showcasing your experience with machine learning infrastructure, including case studies of complex ML systems you've designed, implemented, or maintained.
Examples of your ability to optimize ML infrastructure for performance, scalability, and reliability.
Demonstrations of your ability to collaborate with ML engineers, researchers, and other stakeholders to identify infrastructure requirements and resolve operational issues.

Technical Documentation:

Detailed documentation of your ML infrastructure designs, including architecture diagrams, deployment processes, and monitoring strategies.
Code quality and best practice guidelines for ML infrastructure development and maintenance.
Examples of your ability to mentor and share knowledge with other engineers and team members.

📝 Enhancement Note: As this role involves designing, implementing, and maintaining machine learning infrastructure, a strong portfolio demonstrating your experience with ML systems and infrastructure management is 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, including medical, dental, and vision insurance.
Retirement benefits, including a 403(b) retirement plan with company matching.
Generous time off, including vacation, sick leave, and holidays.
Professional development opportunities, including conference attendance, certification, and community involvement.
A flexible and remote-friendly work environment with a focus on work-life balance.

Working Hours: Wikimedia Foundation staff members typically work a standard 40-hour workweek, with flexibility for deployment windows, maintenance, and project deadlines.

📝 Enhancement Note: Wikimedia Foundation offers a competitive salary and comprehensive benefits package to attract and retain top talent in the machine learning infrastructure field.

🎯 Team & Company Context

🏢 Company Culture

Industry: Wikimedia Foundation operates Wikipedia and other Wikimedia free knowledge projects, focusing on enabling every single human to freely share in the sum of all knowledge.

Company Size: Wikimedia Foundation is a mid-sized organization with a global presence, employing approximately 300 staff members and supporting a large volunteer community.

Founded: Wikimedia Foundation was founded in 2003, with a mission to support and promote free access to knowledge and education worldwide.

Team Structure:

The Machine Learning team is a distributed, global team working across UTC -5 to UTC +3 (Eastern Americas, Europe, and Africa).
The team is composed of ML engineers, researchers, data scientists, and SREs, collaborating closely with product teams, volunteers, and other Wikimedia teams.
The Machine Learning Infrastructure team is a subset of the broader Machine Learning team, focused on designing, implementing, and maintaining the foundational infrastructure that enables Wikimedia's ML engineers and researchers to efficiently train, deploy, and monitor machine learning models in production.

Development Methodology:

Wikimedia Foundation follows Agile methodologies, with a focus on continuous integration, continuous deployment, and continuous improvement.
The Machine Learning team uses Scrum for project management and Git for version control and collaboration.
Wikimedia Foundation emphasizes open-source software development, with a strong commitment to volunteer communities and open collaboration.

📝 Enhancement Note: Wikimedia Foundation's commitment to open-source software and volunteer communities makes it an attractive option for individuals passionate about free knowledge and education.

📈 Career & Growth Analysis

Web Technology Career Level: This role is at the senior staff level, requiring a deep understanding of machine learning infrastructure design, implementation, and maintenance. The ideal candidate will have 10+ years of experience in SRE, DevOps, or infrastructure engineering, with a strong focus on machine learning systems.

Reporting Structure: The Staff SRE (Machine Learning Infrastructure) reports directly to the Director of Machine Learning, Chris Albon. The role involves collaborating with various teams, including ML engineers, researchers, product teams, and other SREs, to ensure high reliability, availability, and scalability of Wikimedia's ML systems.

Technical Impact: As a Staff SRE specializing in ML infrastructure, the primary technical impact of this role is designing, developing, and maintaining the foundational infrastructure that enables Wikimedia's ML engineers and researchers to efficiently train, deploy, and monitor machine learning models in production. This role directly influences the performance, scalability, and reliability of Wikimedia's ML systems, impacting the user experience and the organization's ability to effectively utilize machine learning in its products and services.

Growth Opportunities:

Technical Growth: Deepen your expertise in machine learning infrastructure, emerging technologies, and best practices. Explore opportunities to specialize in specific areas of ML infrastructure, such as scalable training systems, model deployment, or MLOps.
Leadership & Mentoring: Develop your leadership and mentoring skills by guiding team members and sharing knowledge on infrastructure management, operational excellence, and reliability engineering. Explore opportunities to take on more significant technical leadership roles within Wikimedia's Machine Learning team or across the organization.
Product & Domain Expertise: Expand your understanding of Wikimedia's products, services, and user base. Collaborate with product teams and researchers to identify infrastructure requirements and optimize ML workflows for specific domains, such as content recommendation, content generation, or content moderation.

📝 Enhancement Note: Wikimedia Foundation offers significant growth opportunities for individuals looking to advance their careers in machine learning infrastructure, technical leadership, and product domain expertise.

🌐 Work Environment

Office Type: Wikimedia Foundation is a remote-first organization, with staff members working from various locations around the world. The Machine Learning team has a distributed, global presence, with team members working across UTC -5 to UTC +3 (East Coast, Europe, and Africa).

Office Location(s): Wikimedia Foundation has offices in San Francisco, California, USA, and Berlin, Germany. However, most staff members work remotely from various locations worldwide.

Workspace Context:

Wikimedia Foundation provides remote workers with a flexible and supportive work environment, with a focus on work-life balance and employee well-being.
Remote workers are provided with a home office allowance to set up a comfortable and productive workspace.
Wikimedia Foundation encourages open communication, collaboration, and knowledge sharing among team members, regardless of location.

Work Schedule: Wikimedia Foundation staff members typically work a standard 40-hour workweek, with flexibility for deployment windows, maintenance, and project deadlines. The Machine Learning team operates asynchronously, with team members working together across different time zones to ensure high reliability and availability of Wikimedia's ML systems.

📝 Enhancement Note: Wikimedia Foundation's remote-friendly work environment and focus on work-life balance make it an attractive option for individuals seeking a flexible and supportive work arrangement.

📄 Application & Technical Interview Process

Interview Process:

Phone Screen: A brief phone call to discuss your experience, qualifications, and interest in the role.
Technical Deep Dive: A detailed technical conversation focused on your experience with machine learning infrastructure, including architecture design, implementation, and maintenance. This conversation may include live coding or system design exercises.
Behavioral & Cultural Fit: An interview to assess your communication skills, problem-solving abilities, and cultural fit within Wikimedia's global, remote-friendly team.
Final Review: A meeting with the Director of Machine Learning, Chris Albon, to discuss your qualifications, career aspirations, and fit for the role.

Portfolio Review Tips:

Highlight your experience with machine learning infrastructure, including case studies of complex ML systems you've designed, implemented, or maintained.
Showcase your ability to optimize ML infrastructure for performance, scalability, and reliability.
Demonstrate your ability to collaborate with ML engineers, researchers, and other stakeholders to identify infrastructure requirements and resolve operational issues.

Technical Challenge Preparation:

Brush up on your knowledge of machine learning infrastructure, including on-premises and cloud-based solutions.
Review your experience with infrastructure automation, configuration management, and observability tools.
Familiarize yourself with Wikimedia's products, services, and user base to understand the unique challenges and opportunities in ML infrastructure design and maintenance.

📝 Enhancement Note: Wikimedia Foundation's interview process is designed to assess your technical expertise, communication skills, and cultural fit within the organization's global, remote-friendly team. By preparing thoroughly and showcasing your experience with machine learning infrastructure, you can increase your chances of success in the application and interview process.

🛠 Technology Stack & Web Infrastructure

Frontend Technologies: N/A (This role focuses on machine learning infrastructure, not frontend technologies)

Backend & Server Technologies:

Machine Learning Infrastructure: Kubernetes, Docker, GPU acceleration, distributed training systems (e.g., TensorFlow Extended, Kubeflow)
Infrastructure Automation & Configuration Management: Terraform, Ansible, Helm, Argo CD
Observability, Monitoring, & Logging: Prometheus, Grafana, ELK stack (e.g., Elasticsearch, Logstash, Kibana)
Cloud Platforms: Google Cloud Platform (GCP), Amazon Web Services (AWS), Microsoft Azure (Azure)
Machine Learning Frameworks: PyTorch, TensorFlow, scikit-learn
Data Processing & Feature Store Technologies: Apache Beam, Apache Arrow, Temporal
MLOps Platforms & Workflow Orchestration: MLflow, TensorFlow Extended, Kubeflow

Development & DevOps Tools:

Version Control: Git
CI/CD Pipelines: Jenkins, GitHub Actions, GitLab CI/CD
Containerization & Orchestration: Docker, Kubernetes
Infrastructure as Code (IaC): Terraform, CloudFormation
Configuration Management: Ansible, Puppet
Monitoring & Alerting: Prometheus, Grafana, ELK stack

📝 Enhancement Note: Wikimedia Foundation's technology stack is diverse and extensive, reflecting the organization's commitment to open-source software, machine learning, and cutting-edge web infrastructure. Familiarity with Wikimedia's technology stack is crucial for success in this role.

👥 Team Culture & Values

Web Development Values:

Reliability & Availability: Wikimedia Foundation is committed to ensuring high reliability and availability of its ML systems, with a focus on minimizing downtime and maximizing user experience.
Scalability & Performance: Wikimedia Foundation prioritizes designing and implementing ML infrastructure that can scale to meet the demands of its global user base and support the organization's growth and innovation.
Collaboration & Knowledge Sharing: Wikimedia Foundation encourages open communication, collaboration, and knowledge sharing among team members, regardless of location or role.
Innovation & Emerging Technologies: Wikimedia Foundation embraces emerging technologies and trends in machine learning infrastructure, continuously seeking opportunities to improve and optimize its ML systems.

Collaboration Style:

Cross-Functional Integration: Wikimedia Foundation's Machine Learning team works closely with various teams, including product teams, researchers, and volunteers, to identify infrastructure requirements, resolve operational issues, and streamline the ML lifecycle.
Code Review & Peer Programming: Wikimedia Foundation emphasizes code review and peer programming practices to ensure high-quality, maintainable, and secure ML infrastructure.
Knowledge Sharing & Mentoring: Wikimedia Foundation encourages team members to share their knowledge and expertise with others, fostering a culture of continuous learning and improvement.

📝 Enhancement Note: Wikimedia Foundation's commitment to open-source software, collaboration, and knowledge sharing makes it an attractive option for individuals passionate about free knowledge, education, and working in a global, remote-friendly team environment.

⚡ Challenges & Growth Opportunities

Technical Challenges:

Scalable ML Infrastructure: Design and implement robust ML infrastructure that can scale to meet the demands of Wikimedia's global user base and support the organization's growth and innovation.
Reliability & Operations: Ensure high reliability and robust operations of complex, distributed ML systems at scale, minimizing downtime and maximizing user experience.
Emerging Technologies & Trends: Stay up-to-date with emerging machine learning technologies and trends, and incorporate them into Wikimedia's ML infrastructure as appropriate.
User Experience & Performance: Optimize ML infrastructure for performance, scalability, and accessibility, ensuring a seamless user experience for Wikimedia's global audience.

Learning & Development Opportunities:

Technical Skill Development: Deepen your expertise in machine learning infrastructure, emerging technologies, and best practices. Explore opportunities to specialize in specific areas of ML infrastructure, such as scalable training systems, model deployment, or MLOps.
Leadership & Mentoring: Develop your leadership and mentoring skills by guiding team members and sharing knowledge on infrastructure management, operational excellence, and reliability engineering. Explore opportunities to take on more significant technical leadership roles within Wikimedia's Machine Learning team or across the organization.
Product & Domain Expertise: Expand your understanding of Wikimedia's products, services, and user base. Collaborate with product teams and researchers to identify infrastructure requirements and optimize ML workflows for specific domains, such as content recommendation, content generation, or content moderation.

📝 Enhancement Note: Wikimedia Foundation offers significant technical challenges and growth opportunities for individuals looking to advance their careers in machine learning infrastructure, technical leadership, and product domain expertise.

💡 Interview Preparation

Technical Questions:

ML Infrastructure Design & Architecture: Describe your experience designing and implementing scalable ML infrastructure for training, deployment, monitoring, and scaling of machine learning models. Discuss your approach to optimizing ML infrastructure for performance, scalability, and reliability.
System Design & Problem-Solving: Walk through a complex ML system design challenge you've faced in the past, explaining your approach to identifying and resolving operational issues, and the outcome of your efforts.
Collaboration & Stakeholder Management: Share an example of a successful collaboration with ML engineers, researchers, or other stakeholders to identify infrastructure requirements, resolve operational issues, and streamline the ML lifecycle.

Company & Culture Questions:

Wikimedia's Mission & Values: Explain why you're drawn to Wikimedia's mission of enabling every single human to freely share in the sum of all knowledge, and how your work aligns with the organization's values and culture.
Global & Remote Work: Describe your experience working in a global, remote-friendly team environment, and how you've adapted to collaborating with team members across different time zones and cultures.
Adaptability & Continuous Learning: Discuss your approach to staying up-to-date with emerging machine learning technologies and trends, and how you've applied this knowledge to improve and optimize ML infrastructure in previous roles.

Portfolio Presentation Strategy:

Case Studies: Highlight your experience with machine learning infrastructure by presenting case studies of complex ML systems you've designed, implemented, or maintained. Focus on the challenges you faced, your approach to resolving operational issues, and the outcome of your efforts.
Live Demos: Showcase your ability to optimize ML infrastructure for performance, scalability, and reliability by demonstrating live examples of your work. Highlight your use of automation, configuration management, and observability tools to streamline the ML lifecycle.
Technical Deep Dive: Prepare to discuss the technical details of your portfolio projects, including architecture design, implementation, and maintenance. Be ready to answer questions about your approach to ML infrastructure, system design, and problem-solving.

📝 Enhancement Note: Wikimedia Foundation's interview process is designed to assess your technical expertise, communication skills, and cultural fit within the organization's global, remote-friendly team. By preparing thoroughly and showcasing your experience with machine learning infrastructure, you can increase your chances of success in the application and interview process.

📌 Application Steps

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

Submit Your Application: Click the application link provided in the job listing to submit your resume, portfolio, and any other required documents.
Prepare Your Portfolio: Tailor your portfolio to highlight your experience with machine learning infrastructure, including case studies of complex ML systems you've designed, implemented, or maintained. Showcase your ability to optimize ML infrastructure for performance, scalability, and reliability.
Optimize Your Resume: Tailor your resume to emphasize your relevant experience with machine learning infrastructure, infrastructure automation, and observability tools. Highlight your proficiency with machine learning frameworks, data processing technologies, and MLOps platforms.
Research Wikimedia Foundation: Familiarize yourself with Wikimedia Foundation's products, services, and user base to understand the unique challenges and opportunities in ML infrastructure design and maintenance. Prepare for interview questions about Wikimedia's mission, values, and global, remote-friendly team culture.
Prepare for Technical Challenges: Brush up on your knowledge of machine learning infrastructure, including on-premises and cloud-based solutions. Review your experience with infrastructure automation, configuration management, and observability tools. Familiarize yourself with Wikimedia's technology stack and the specific requirements of the role.

📝 Enhancement Note: Wikimedia Foundation's application process is designed to assess your technical expertise, communication skills, and cultural fit within the organization's global, remote-friendly team. By preparing thoroughly and showcasing your experience with machine learning infrastructure, you can increase your chances of success in the application and interview process.

Staff Site Reliability Engineer