Background jobs power some of the most critical parts of modern applications: sending emails, processing payments, generating reports, running ML pipelines, and synchronising external services, etc. While the initial setup is often simple, scaling these systems introduces a range of reliability challenges. </br> In this talk, we’ll explore common failure modes that appear as Python worker systems grow: retry storms, job duplication, long-running tasks blocking queues, and cascading failures when downstream services slow down. Drawing from real production lessons, we’ll examine patterns that make background job systems more resilient. </br> The session will cover techniques such as designing idempotent jobs, handling retries safely, preventing queue congestion, and monitoring worker health. We’ll also discuss architectural considerations around task queues, worker isolation, and backpressure management. </br> By the end of the talk, attendees will understand strategies for designing background job systems that remain reliable as workloads scale.