Senior Site Reliability Engineer (SRE)

About the Role:

Site Reliability Engineering (SRE) at Tubi is not a traditional operations team. We are a software engineering organization that applies a developer's mindset and toolkit to the challenges of building and running large-scale, distributed systems. Our mission is to engineer resilience from the ground up, enabling our product teams to innovate rapidly while ensuring our users have a stellar experience. We own the availability, latency, performance, and capacity of our platform, and we achieve our goals through a culture of data-driven decision-making, blameless learning, and relentless automation. This is a hybrid role based out of our Toronto office. You must be willing to travel to our Toronto office two days/week.

What You'll Do:

• System Architecture & Design: Design, build, and maintain scalable, highly available, and fault-tolerant distributed systems. Partner with development teams as a reliability consultant, reviewing designs and influencing architectural decisions to ensure new services are built with reliability, observability, and performance as core principles, not afterthoughts.
• Automation & Software Development: Write robust, performant, and maintainable code to automate operational tasks, and CI/CD pipelines. Build the internal tools, libraries, and frameworks that enable engineering teams to self-service their observability needs, reducing cognitive load and increasing their velocity.
• Incident Response & Post-Mortem Analysis: Participate in a 24/7 on-call rotation, acting as a key technical leader and incident commander during critical service disruptions. Conduct deep, blameless root cause analyses (RCAs) that go beyond immediate fixes to identify and address systemic issues. Drive the implementation of corrective actions to prevent the recurrence of incidents.
• Performance & Capacity Planning: Proactively monitor, measure, and optimize system performance to ensure low latency and high efficiency. Gather and analyze metrics from operating systems and applications to assist in performance tuning and fault finding. Analyze usage patterns and historical data to forecast capacity needs, ensuring our platform stays ahead of customer demand.
• Building AI-Driven Automation: Building and integrating solutions that leverage our AIOps platform. This involves writing the code that consumes signals from the AI system, correlates disparate data sources, automates responses to AI-detected anomalies, and builds self-healing systems triggered by predictive alerts. You will transform AI insights into concrete reliability improvements.
• Leveraging AI for Code Development: Utilizing AI-assisted coding tools (e.g., Claude Code, Cursor) as a force multiplier in your daily workflow. You will leverage these assistants to write high-quality automation scripts, Terraform modules, Kubernetes manifests, and observability dashboards faster and more efficiently, while applying your expertise to validate and refine their output.
• Enriching our AI Knowledge Base: Developing and enriching our observability platform's internal knowledge base. You will be responsible for creating and documenting high-quality runbooks and procedural guides that can be ingested and used by AI assistants to provide context-aware troubleshooting guidance to the on-call engineer during an incident.
• Applying Data Science to Reliability: Treating reliability as a data science problem. You will analyze vast sets of telemetry data to identify trends, build predictive models for system capacity, and proactively identify performance bottlenecks and potential failure modes before they can impact our users.

Your Background:

• Bachelor's degree in Computer Science, a related technical field, or equivalent practical experience.
• 5+ years of professional experience in a Site Reliability Engineering, DevOps, or Software Engineering role with a focus on infrastructure and operations.
• Strong programming proficiency in one or more high-level languages such as Rust, Go, Python, or Typescript. You should be comfortable writing, testing, and deploying production-grade code.
• Deep knowledge of AWS services (especially networking, IAM, EKS, ALBs/NLBs, Route 53, CloudWatch).
• Proven experience with Kubernetes in production (EKS preferred), including service exposure, networking, and availability engineering.
• A solid understanding of Linu