Staff Site Reliability Engineer

Wikimedia Foundation
Full_time•$129k-201k/year (USD)

📍 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: 2025-06-18
  • Experience Level: 10+ years
  • Remote Status: Remote Solely

🚀 Role Summary

The Wikimedia Foundation is seeking a Staff Site Reliability Engineer (SRE) specializing in Machine Learning Infrastructure to join their distributed team. This role focuses on 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.

As a Staff SRE, you will collaborate closely with various teams to identify infrastructure requirements, resolve operational issues, and streamline the ML lifecycle. Your primary goal will be to ensure high service quality, reliability, and availability of ML infrastructure.

📝 Enhancement Note: This role requires a strong background in Site Reliability Engineering, DevOps, or infrastructure engineering, with substantial exposure to production-grade machine learning systems. Familiarity with popular Python-based ML frameworks is also expected.

💻 Primary Responsibilities

  • Design and Implement ML Infrastructure: Develop and maintain robust ML infrastructure for training, deployment, monitoring, and scaling of machine learning models.
  • Collaborate with Teams: Work 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 Monitor and Optimize Systems: Continuously monitor and optimize system performance, capacity, and security to maintain high service quality.
  • Provide Expert Guidance: Offer technical guidance and documentation to teams across Wikimedia to effectively utilize the ML infrastructure and best practices.
  • Mentor Team Members: Share knowledge on infrastructure management, operational excellence, and reliability engineering with team members.

📝 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 of 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, or equivalent experience.

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:

  • Demonstrated expertise in creating robust tooling and automation solutions that simplify the deployment, management, and monitoring of ML infrastructure.
  • Experience with cloud-based ML infrastructure (e.g., AWS, GCP, Azure) and managed ML services (e.g., SageMaker, AI Platform, Azure Machine Learning).
  • Familiarity with the Wikimedia ecosystem and open-source software development processes.

📝 Enhancement Note: While not required, experience with cloud-based ML infrastructure and managed ML services can be beneficial for this role, as Wikimedia is exploring the integration of cloud-based solutions into their ML infrastructure.

📊 Web Portfolio & Project Requirements

Portfolio Essentials:

  • A well-structured portfolio showcasing your experience with on-premises ML infrastructure, including examples of Kubernetes clusters, GPU-accelerated workloads, and distributed training systems.
  • Documentation of your experience with infrastructure automation and configuration management tools, highlighting your ability to streamline deployment and management processes.
  • Examples of your expertise in implementing observability, monitoring, and logging for ML systems, demonstrating your ability to ensure high service quality and reliability.

Technical Documentation:

  • Detailed documentation of your approach to ML infrastructure design, including your methodology for ensuring scalability, reliability, and performance.
  • Case studies of your experience resolving operational issues and optimizing ML systems, highlighting your problem-solving skills and technical acumen.
  • Examples of your mentoring and knowledge-sharing efforts, showcasing your ability to collaborate effectively with team members and contribute to their professional growth.

📝 Enhancement Note: As this role involves working with a diverse, global team, your portfolio should emphasize your ability to communicate effectively and collaborate with team members across different time zones and cultural backgrounds.

💵 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 company matching.
  • Generous paid time off, including vacation, sick leave, and holidays.
  • Family-friendly benefits, including parental leave and adoption assistance.
  • A flexible, remote-friendly work environment with a focus on work-life balance.
  • Opportunities for professional development and growth, including conference attendance, certification, and community involvement.

Working Hours: This role requires a commitment to Wikimedia's global team, with a flexible work schedule that accommodates collaboration across UTC -5 to UTC +3 (Eastern Americas, Europe, and Africa).

📝 Enhancement Note: Wikimedia Foundation offers a competitive salary and benefits package that is designed to attract and retain top talent in the field of machine learning infrastructure. The salary range provided is an estimate and may vary based on the candidate's location, experience, and other factors.

🎯 Team & Company Context

🏢 Company Culture

Industry: Wikimedia Foundation is a non-profit organization dedicated to providing free access to the sum of all human knowledge through its flagship project, Wikipedia, and other Wikimedia projects.

Company Size: Wikimedia Foundation is a mid-sized organization with a global presence, employing over 300 staff members and supporting a vast community of volunteers.

Founded: Wikimedia Foundation was established in 2003 in St. Petersburg, Florida, and is now headquartered in San Francisco, California.

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 consists of Machine Learning Engineers, Researchers, and Site Reliability Engineers, collaborating closely with various Wikimedia teams, including Product, Design, and Community.
  • The Machine Learning team reports directly to the Director of Machine Learning, Chris Albon.

Development Methodology:

  • Wikimedia Foundation follows Agile development methodologies, with a focus on iterative improvement, collaboration, and continuous delivery.
  • The Machine Learning team uses Git for version control, JIRA for project management, and a variety of tools for communication and collaboration, including Slack, Google Workspace, and Wikimedia's internal communication platforms.

Company Website: Wikimedia Foundation

📝 Enhancement Note: Wikimedia Foundation's culture is characterized by its commitment to open-source software, volunteer communities, and free knowledge. The organization values collaboration, innovation, and a user-centric approach to its projects and services.

📈 Career & Growth Analysis

Web Technology Career Level: This role is a senior-level position within the Site Reliability Engineering and Machine Learning Infrastructure domains. It requires a deep understanding of scalable infrastructure design, production-grade machine learning systems, and proven expertise in ensuring high reliability and robust operations.

Reporting Structure: The Staff SRE reports directly to the Director of Machine Learning, Chris Albon, and collaborates closely with various Wikimedia teams, including Machine Learning Engineers, Product, Design, and Community.

Technical Impact: This role has a significant impact on Wikimedia's Machine Learning infrastructure, ensuring high service quality, reliability, and availability for internal ML engineers and researchers. The Staff SRE's work directly influences the efficiency and effectiveness of machine learning workflows across Wikimedia's projects.

Growth Opportunities:

  • Technical Leadership: As a Staff SRE, you will have the opportunity to mentor team members, contribute to technical decision-making, and drive the evolution of Wikimedia's ML infrastructure.
  • Architecture and Design: You will be encouraged to explore and implement innovative solutions that improve the scalability, reliability, and performance of Wikimedia's ML infrastructure.
  • Emerging Technologies: Wikimedia is actively exploring the integration of cloud-based ML infrastructure and managed ML services into its ecosystem. This role presents an excellent opportunity to gain experience with emerging technologies and contribute to their adoption within Wikimedia.

📝 Enhancement Note: Wikimedia Foundation offers numerous growth opportunities for technical professionals, including mentorship, leadership development, and architecture decision-making. The organization values a culture of continuous learning and encourages its team members to explore new technologies and approaches to problem-solving.

🌐 Work Environment

Office Type: Wikimedia Foundation is a remote-first organization, with a global workforce distributed across multiple time zones.

Office Location(s): Wikimedia Foundation has offices in San Francisco, California, and various remote locations across the globe. However, this role does not require on-site presence, and the successful candidate can work from any location within the approved countries.

Workspace Context:

  • Wikimedia Foundation provides its remote employees with a comprehensive remote work setup, including ergonomic furniture, high-quality audio-visual equipment, and necessary software tools.
  • The organization encourages a flexible work schedule that balances work-life balance and accommodates collaboration across time zones.
  • Wikimedia Foundation fosters a collaborative work environment, with regular team meetings, virtual events, and social activities designed to build camaraderie and strengthen team bonds.

Work Schedule: Wikimedia Foundation's global team works across UTC -5 to UTC +3 (Eastern Americas, Europe, and Africa). The organization encourages a flexible work schedule that balances work-life balance and accommodates collaboration across time zones.

📝 Enhancement Note: Wikimedia Foundation's remote-friendly work environment is designed to support the needs of its global team, with a focus on flexibility, collaboration, and work-life balance.

📄 Application & Technical Interview Process

Interview Process:

  1. Technical Phone Screen (60 minutes): A phone or video call to assess your technical skills, experience, and cultural fit with Wikimedia.
  2. Technical Deep Dive (90 minutes): A deeper exploration of your technical expertise, including a discussion of your portfolio, architecture decisions, and problem-solving approaches.
  3. Behavioral and Cultural Fit Interview (60 minutes): An interview focused on your communication skills, collaboration style, and alignment with Wikimedia's values and culture.
  4. Final Review and Decision (30 minutes): A final review of your application materials and a discussion of your fit for the role and Wikimedia's organization.

Portfolio Review Tips:

  • Highlight your experience with on-premises ML infrastructure, including Kubernetes clusters, GPU-accelerated workloads, and distributed training systems.
  • Demonstrate your proficiency with infrastructure automation and configuration management tools, showcasing your ability to streamline deployment and management processes.
  • Include examples of your expertise in implementing observability, monitoring, and logging for ML systems, emphasizing your commitment to high service quality and reliability.
  • Tailor your portfolio to Wikimedia's projects and user base, emphasizing your understanding of the organization's mission and values.

Technical Challenge Preparation:

  • Brush up on your knowledge of Kubernetes, Docker, GPU acceleration, and distributed training systems.
  • Familiarize yourself with infrastructure automation and configuration management tools, such as Terraform, Ansible, Helm, and Argo CD.
  • Prepare for questions related to machine learning frameworks, including PyTorch, TensorFlow, and scikit-learn.
  • Practice explaining complex technical concepts in a clear and concise manner, as you will be expected to communicate effectively with team members across different technical backgrounds.

ATS Keywords: Kubernetes, Docker, GPU Acceleration, Distributed Training Systems, Terraform, Ansible, Helm, Argo CD, Prometheus, Grafana, ELK Stack, PyTorch, TensorFlow, scikit-learn, Site Reliability Engineering, DevOps, Infrastructure Engineering, Machine Learning Infrastructure, Cloud-Based ML Infrastructure, Managed ML Services, Wikimedia Foundation, Open-Source Software, Volunteer Communities, Free Knowledge.

📝 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. The organization values a diverse, global team and encourages applicants from all backgrounds to apply.

🛠 Technology Stack & Web Infrastructure

Frontend Technologies: N/A (This role focuses on backend and infrastructure technologies)

Backend & Server Technologies:

  • Machine Learning Infrastructure: Kubernetes, Docker, GPU acceleration, distributed training systems (e.g., TensorFlow Extended, Kubeflow Pipelines)
  • Infrastructure Automation: Terraform, Ansible, Helm, Argo CD
  • Observability, Monitoring, and Logging: Prometheus, Grafana, ELK Stack
  • Machine Learning Frameworks: PyTorch, TensorFlow, scikit-learn

Development & DevOps Tools:

  • Version Control: Git
  • Project Management: JIRA
  • Communication and Collaboration: Slack, Google Workspace, Wikimedia's internal communication platforms

📝 Enhancement Note: Wikimedia Foundation's technology stack is designed to support the organization's mission of providing free access to the sum of all human knowledge. The organization values open-source software and encourages the use of industry-standard tools and technologies.

👥 Team Culture & Values

Web Development Values:

  • User-Centric: Wikimedia Foundation prioritizes the user experience and strives to create intuitive, accessible, and engaging web interfaces.
  • Open and Collaborative: Wikimedia Foundation values open-source software, volunteer communities, and fosters a culture of collaboration and knowledge-sharing.
  • Innovative: Wikimedia Foundation encourages experimentation, iteration, and continuous improvement in its web projects and services.
  • User-Focused: Wikimedia Foundation prioritizes the needs and preferences of its users, ensuring that its web projects and services meet their expectations and requirements.

Collaboration Style:

  • Cross-Functional: Wikimedia Foundation encourages collaboration across teams, with regular communication and coordination between web development, design, product, and community teams.
  • Iterative: Wikimedia Foundation follows Agile development methodologies, with a focus on continuous improvement and rapid iteration.
  • User-Centric: Wikimedia Foundation prioritizes user feedback and involvement in its web projects and services, ensuring that they meet the needs and expectations of its diverse user base.

📝 Enhancement Note: Wikimedia Foundation's culture is characterized by its commitment to open-source software, volunteer communities, and a user-centric approach to its projects and services. The organization values collaboration, innovation, and a culture of continuous learning and improvement.

🌐 Challenges & Growth Opportunities

Technical Challenges:

  • Scalability: Wikimedia's ML infrastructure must be designed to handle the organization's growing user base and the increasing complexity of its machine learning workloads.
  • Reliability and Availability: Wikimedia's ML infrastructure must ensure high service quality, reliability, and availability, with minimal downtime and maximum performance.
  • Security and Compliance: Wikimedia's ML infrastructure must comply with relevant data protection regulations and maintain the security and privacy of its users' data.
  • Cost Optimization: Wikimedia's ML infrastructure must be designed and maintained in a cost-effective manner, ensuring optimal resource utilization and minimizing waste.

Learning & Development Opportunities:

  • Emerging Technologies: Wikimedia is actively exploring the integration of cloud-based ML infrastructure and managed ML services into its ecosystem. This role presents an excellent opportunity to gain experience with emerging technologies and contribute to their adoption within Wikimedia.
  • Technical Mentorship: Wikimedia Foundation offers mentorship opportunities for technical professionals, with a focus on knowledge-sharing, skill development, and career progression.
  • Leadership Development: Wikimedia Foundation encourages its team members to develop their leadership skills and contribute to technical decision-making, architecture design, and infrastructure evolution.

📝 Enhancement Note: Wikimedia Foundation offers numerous growth opportunities for technical professionals, including mentorship, leadership development, and architecture decision-making. The organization values a culture of continuous learning and encourages its team members to explore new technologies and approaches to problem-solving.

💡 Interview Preparation

Technical Questions:

  • Infrastructure Design: Discuss your approach to designing scalable, reliable, and secure ML infrastructure for high-performance machine learning training and inference workloads.
  • Problem-Solving: Describe your experience resolving operational issues and optimizing ML systems, highlighting your problem-solving skills and technical acumen.
  • Architecture Decisions: Explain your methodology for making architecture decisions, emphasizing your ability to balance cost, performance, and scalability considerations.

Company & Culture Questions:

  • Wikimedia's Mission: Explain why you are drawn to Wikimedia's mission of providing free access to the sum of all human knowledge and how your work aligns with this goal.
  • Open-Source Software: Discuss your experience with open-source software and your commitment to Wikimedia's values of collaboration, knowledge-sharing, and community involvement.
  • User-Centric Approach: Describe your approach to creating user-centric web projects and services, emphasizing your understanding of user needs, preferences, and behaviors.

Portfolio Presentation Strategy:

  • Portfolio Structure: Organize your portfolio to highlight your experience with on-premises ML infrastructure, infrastructure automation, and observability, monitoring, and logging for ML systems.
  • Case Studies: Include detailed case studies of your experience resolving operational issues, optimizing ML systems, and implementing innovative solutions.
  • Technical Deep Dive: Prepare to discuss the technical details of your portfolio projects, emphasizing your expertise in ML infrastructure, architecture, and problem-solving.

📝 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. The organization values a diverse, global team and encourages applicants from all backgrounds to apply.

📌 Application Steps

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

  1. Submit Your Application: Click the "Apply for Job" button on the job listing page to submit your application through the application link provided.
  2. Tailor Your Resume: Customize your resume to highlight your relevant experience, skills, and accomplishments in Site Reliability Engineering, DevOps, infrastructure engineering, and machine learning infrastructure.
  3. Prepare Your Portfolio: Ensure your portfolio showcases your experience with on-premises ML infrastructure, infrastructure automation, and observability, monitoring, and logging for ML systems. Tailor your portfolio to Wikimedia's projects and user base, emphasizing your understanding of the organization's mission and values.
  4. Practice Technical Challenges: Brush up on your knowledge of Kubernetes, Docker, GPU acceleration, distributed training systems, infrastructure automation and configuration management tools, and machine learning frameworks. Prepare for questions related to machine learning infrastructure, architecture, and problem-solving.
  5. Research Wikimedia Foundation: Familiarize yourself with Wikimedia Foundation's mission, values, and projects. Prepare thoughtful answers to questions about the organization's commitment to open-source software, volunteer communities, and free knowledge.

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

Application Requirements

Candidates should have 7+ years of experience in SRE or related roles with expertise in production-grade machine learning systems. Strong proficiency in infrastructure automation and familiarity with Python-based ML frameworks is also required.