The Technical Round
This is the one round in this whole guide where the format genuinely depends on who’s hiring. Google-style companies still run data-structures-and-algorithms coding screens, especially for early-career candidates. AI-native and mid-market companies lean instead toward debugging a broken observability pipeline, writing automation, or defending an SLO design out loud. Both are real — know which one you’re walking into before you prep.
What to expect
Six problem shapes show up across current postings and interview reports. Which one you get depends heavily on the company — a frontier lab hiring for a generalist infrastructure org skews toward the first; an AI-native company hiring specifically for LLM-serving reliability skews toward the rest.
| Problem type | What it looks like |
|---|---|
| DSA coding screen (Google-style) | Standard data-structures-and-algorithms problems, evaluated for correctness and complexity — most common for early-career and campus hiring, less common the more senior and AI-specific the posting gets. |
| Debugging a broken observability pipeline | You’re handed a dashboard or trace where a metric is missing, an alert never fired, or a trace is dropping spans — find why and fix it while narrating your reasoning. |
| SLI/SLO design exercise | Given a service description (say, an LLM inference endpoint), propose SLIs, set an SLO, and defend the error-budget trade-off against a pushy “why not 99.99%?” follow-up. |
| Incident-triage scripting | Write a quick script to correlate logs and traces across a handful of services to find which one is the actual source of an alert storm — the kind of tool-building the job runs on daily. |
| Infra-as-code exercise | Debug or extend a small Terraform or Kubernetes manifest — the bar is fluency with the habit of IaC, not memorized syntax. |
| Take-home build | A smaller number of companies replace the live round with a take-home: instrument a small service with tracing and an SLO dashboard, then write up the trade-offs. |
If you’re unsure which format a specific company runs, ask the recruiter directly — “is this round closer to algorithmic coding or closer to operational debugging?” is a completely normal question at this stage, and the answer changes how you should spend the next two weeks.
What actually separates a pass from a fail
Across both formats, interviewers consistently describe the same gap between strong and weak candidates — and it’s rarely about raw technical correctness:
Operator framing, not developer framing
“I’d mitigate the impact first, then investigate” reads as real operational experience. “I’d fix the bug” — jumping straight to root cause before containing blast radius — is the single most common tell of inexperience, even when the eventual fix is correct.
Asking about blast radius before diving in
Who’s affected, how badly, and is it getting worse — asked before touching anything — shows the instinct the job actually requires: contain first, understand fully second.
Distrust of a single green dashboard
For an AI-serving system, “latency and availability look fine” isn’t the end of the investigation. Asking about output-quality or cost metrics on top of the traditional ones reads as understanding the job’s actual failure surface.
Building the fix as a script or automation, not a one-off command
A candidate who solves the immediate problem by hand, with no thought to whether it’ll recur, misses the entire point of the discipline — reducing toil is close to the job’s definition.
Honest trade-off calls, said out loud
“I’d add retries with backoff here in a real system, but I’ll skip it given the time” beats silently skipping it, and beats silently gold-plating a throwaway exercise too.
Most candidates fail on operational judgment, not technical knowledge. A 2026 recruiter-sourced guide to SRE interviews put this bluntly, and it matches what shows up in Google’s own published interview guidance: the hardest rounds consistently center on error-budget policy decisions and live production debugging under observation, not on whether you know the right algorithm.
How to prepare
Two weeks is enough if you’re already comfortable with infrastructure fundamentals. The goal isn’t learning new syntax — it’s rebuilding the specific muscle of narrating triage-and-mitigate reasoning under time pressure, in whichever format you’re about to face.
- 1
Week 1 — find out which format you’re actually facing
Ask the recruiter directly, or check recent Glassdoor/Blind reports for that specific company. Prepping DSA drills for a company that actually runs a debugging round (or the reverse) wastes the two weeks you have.
- 2
Week 1 (observability track) — rebuild fluency with a real tracing tool
Set up LangSmith, Langfuse, or Helicone on a small agent from this curriculum’s Advanced Chapter 3 (Observability) and deliberately break something — a dropped span, a missing metric — then practice diagnosing it from the trace alone.
- 3
Week 2 — five incident drills, five SLO-design reps
Take five small “here’s an alert, here’s a dashboard” scenarios and practice the full loop out loud: triage, mitigate, root-cause, propose a prevention. Pair it with five reps of proposing an SLO for a hypothetical service and defending the number against pushback — this shows up in nearly every version of this round.
Two things to have ready before you walk in
Beyond drilling problems, two concrete artifacts change how the whole interview reads, because they let the interviewer evaluate real judgment instead of a simulated forty-five minutes of it.
A real observability setup you can walk someone through
Deploy one of this curriculum’s Intermediate or Advanced agent builds with real tracing wired up — LangSmith, Langfuse, or raw OpenTelemetry. Being able to open an actual trace and point at a real span beats describing what tracing would look like.
An SLO you wrote for something real, even a side project
Have one concrete example ready: what SLI you picked, what SLO you set, and one time the error budget actually mattered — a burn that changed a decision, even a small one.
A two-sentence version of a real production issue you diagnosed
Save the full story for the behavioral round, but have the short version ready — it comes up as a natural follow-up to the technical exercise itself, almost regardless of format.
Neither needs to be impressive at scale. A small, honestly-instrumented, actually-running thing beats an ambitious, half-finished one every time this comes up as a talking point.