hardNewOpenAIAnthropicCoreWeave

What would you build in your first 90 days on this team?

A confident plan built before you know anything is the failure mode, and so is refusing to answer until you have looked. The structure that handles both: a dated shape with the decision points named, one committed win, and the conditions that would change everything after day 30.

Updated Sep 2026 · Grounded in real AI infrastructure interview loops and written to a senior-engineer editorial bar, with every number worked and every diagram hand-built.

20 answers per topic instead of 10, plus saved progress and bookmarks · no cardor unlock all 283 remaining answers · ₹2,000 / $25

The concepts behind this question

Ranked by how closely each one overlaps this question's topic, so the first card is the thing to read if the answer above moved too fast.

Advanced

🧭 Ownership & Judgment🔒 Premium

Leveling Signals: Senior vs StaffThe same loop decides two things: whether you are hired and at what level, and the second is decided by a small set of signals interviewers are trained to listen for. Senior is scope you own and execute; staff is scope you define, across teams, under ambiguity, with the organization's defaults changed behind you. The signals are audible in every round: how you frame a design prompt, which failure modes you name unprompted, whose problem an incident was, what changed after it, and what you chose not to do. This page lists the signals per round, the stories each level needs, and the down-leveling traps.

Foundational

🧭 Ownership & Judgment

Deciding Under Incomplete InformationMost infrastructure decisions are made before the evidence is complete, and the skill being assessed is not judgment in the abstract but classification: whether the decision can be undone cheaply. Reversible decisions deserve speed and a scheduled review; one-way decisions deserve the delay and a second opinion. Engineers who apply the same deliberation to both are slow where speed is free and careless where it is not.

Advanced

🧭 Ownership & Judgment🔒 Premium

Your View on Where AI Infrastructure Is GoingSomewhere in a senior or staff loop an interviewer asks what you think happens next: to GPUs and their challengers, to training at scale, to inference economics, to the tools. It looks like small talk and it is scored. The answer that works is a thesis with a date on it, a reason grounded in numbers you can derive, the counterargument you find strongest, and the thing you would watch to know you were wrong. This page shows how to build such a thesis from the material on this site, gives three worked examples, and lists the answers that sound informed and fail.

Foundational

🧮 Open Weights & Serving Engines

Capacity Planning for Open-Weights FleetsPlanning a fleet for a sparse open-weights model works differently from planning one for a dense model, because memory follows total parameters and throughput follows active parameters, and those now differ by more than twenty times. The sizing goes in one direction only: from a traffic forecast to tokens per second, to replicas at a measured operating point, to GPUs, to racks and kilowatts. Doing it in the other direction, from an available GPU count, produces a fleet that fits the hardware rather than the demand.

Explore the full curriculum →

UP NEXT ON YOUR JOURNEY

Next in this trackTell me about an isolation or access problem you found before anyone else did.Next in this trackYou can see that capacity will fall short in four months. How do you tell leadership?Next in this trackWhere do you think AI infrastructure is going over the next five years?

FEDITOR'S NOTE

Scored on the plan being conditional on what the first month finds, on one small committed win rather than a large proposal, and on naming what would change the plan.

DISCUSSION · 0

No comments yet — be the first to share your approach.