The Staff Engineer's Judgment
Plenty of senior engineers are better at Kubernetes than the staff engineer beside them. What differs is how much depends on the decision, over what horizon, how many other people it constrains, and how expensive it is to reverse. This course teaches that judgment through real infrastructure decisions: every lesson gives the competent senior answer, then the staff answer, then what the second one saw that the first did not.
What you'll learn
Curriculum
7 modules · 31 lessonsWhat Actually Changes
Scope of consequence rather than depth of skill. The four multipliers, what the ladder actually means, and the decisions nobody is making.
Deciding Under Ambiguity
Problems without a spec, choosing what not to build, reversibility, deciding on a deadline, and the right answer nobody will accept.
Technical Judgment at Staff Scope
Buy or build, boundaries, migrations, designing for the org, cost as a constraint, and betting on young technology.
Multiplying Other Engineers
Design review, writing that moves decisions, the platform as a human interface, mentoring that scales, and being wrong in public.
Operating Across the Org
Making the case for infrastructure work, working with product and finance, incident leadership, and saying no.
The Staff Traps
Four failure modes described honestly enough to be recognisable in yourself.
Capstones
Three full walkthroughs, ending with an audit of the reader's own scope.
About the Author
Sharon Sahadevan
AI Infrastructure Engineer
Building production GPU clusters on Kubernetes. H100s, large-scale model serving, and end-to-end ML infrastructure across Azure and AWS.
10+ years designing cloud-native platforms with deep expertise in Kubernetes orchestration, GitOps (Argo CD), Terraform, and MLOps pipelines for LLM deployment.
Author of KubeNatives, a weekly newsletter read by 3,000+ DevOps and ML engineers for production insights on K8s internals, GPU scheduling, and model-serving patterns.