Senior Manager, Site Reliability Engineering

优缺点分析

角色解读

• This role offers a path to senior engineering leadership, potentially advancing to Director or VP-level positions overseeing broader infrastructure, platform engineering, or technology strategy. The focus on building an AI-native SRE function positions the leader at the forefront of operational innovation, opening opportunities in AI/ML operations, technical strategy, or specialized consulting. Success could also lead to expanded responsibilities within Fox Corporation's broader media technology groups.
• Lead and grow a new SRE team, setting the strategic vision for reliability and observability at Tubi. Drive the integration of AI and machine learning (AIOps) into the SRE function to enable predictive maintenance and automated anomaly detection. Own the end-to-end availability, performance, and efficiency of critical user-facing services, managing incidents, budgets, and vendor relationships.
• Deep technical expertise in SRE principles (SLIs/SLOs, error budgets), cloud infrastructure (AWS, especially EKS), and modern tooling (Kubernetes, Terraform, Prometheus). Strong leadership and people management skills to mentor engineers and foster a blameless learning culture. Strategic thinking and communication skills to define multi-year roadmaps, influence cross-functional peers, and articulate technical concepts to diverse stakeholders.

申请策略

• Research Tubi's streaming platform, its scale, and any public technical blog posts to understand their current tech challenges. Prepare to discuss how you would approach the specific 'AI Mandate'—developing an initial strategy for integrating AI into their observability stack. Emphasize alignment with their stated culture of 'blameless learning,' 'data-driven decision-making,' and 'relentless automation' in your cover letter and interviews.
• Quantify your impact in previous SRE/leadership roles using metrics like reduced MTTR, improved SLO attainment, cost savings from automation, or team growth. Highlight specific experience with the listed tech stack (AWS, Kubernetes, Terraform, Prometheus/Grafana) and any prior work with AI/ML concepts or tools in an operational context. Demonstrate strategic leadership by detailing how you've defined technical roadmaps, managed budgets/vendors, and influenced cross-functional partners to adopt SRE practices.
• If lacking, gain familiarity with core AIOps concepts, use cases for predictive monitoring, and the capabilities of tools mentioned (e.g., exploring Claude Code/Cursor for automation). Brush up on advanced Kubernetes topics (networking, security, scalability on EKS) and AWS cost optimization strategies relevant to large-scale streaming services. Practice articulating complex technical strategies and their business value (ROI) to non-technical audiences.

面试指南

• Use the STAR method (Situation, Task, Action, Result) for behavioral questions, focusing on your leadership actions and quantifiable outcomes. For strategic questions, structure your answer by first understanding the goal, then outlining key phases (assessment, pilot, scaling), addressing risks, and defining success metrics. Always link technical decisions back to business objectives (user experience, cost efficiency, innovation velocity).
• Walk me through how you would design and implement a strategy to integrate AI/ML (AIOps) into our existing SRE observability practices. Describe a time you had to manage a major production incident. What was your role, how did you communicate, and what systemic fixes did you implement? How do you balance the need for rapid feature development (using error budgets) with maintaining high reliability standards? How would you build and mentor a new SRE team, and what are your key principles for creating a sustainable on-call rotation? Tell me about your experience managing an SRE budget and negotiating with vendors for tooling.
• Prepare 2-3 detailed case studies from your past experience that cover team building, incident management, technical strategy execution, and cross-functional influence. Be ready to whiteboard or discuss a high-level architecture for a reliable, observable microservices system on AWS/EKS. Research and formulate thoughtful questions about Tubi's specific reliability challenges, their current AI initiatives, and the team's relationship with other engineering groups.