MLOps / ML Platform Engineer Interview Prep & Rubrics

Platform-side ML: reproducibility, feature and training infrastructure, registries and CI/CD for models, serving, drift, and the cost of a GPU fleet.

The loop, in order

MLOps & ML Platform24 questions · 8 topicsEverything between a model that works in a notebook and one a business depends on: reproducibility, feature and training infrastructure, registries, serving, drift, cost and governance.
Machine Learning24 questions · 8 topicsThe modelling half of an ML loop: supervised and unsupervised methods, features and leakage, metric choice, imbalance, deep learning foundations, forecasting and ranking.
DevOps & Cloud37 questions · 8 topicsPlatform and infrastructure questions for DevOps, SRE, and any backend role expected to own its service in production.
Python20 questions · 7 topicsPython for backend, automation, and data roles, including the runtime behaviour that surprises candidates who only know the syntax.
Databases & SQL25 questions · 8 topicsQuery-writing rounds and the storage-engine reasoning behind why a query is slow, for backend, data, and DBA roles alike.
System Design63 questions · 12 topicsOpen-ended design rounds broken into the building blocks they draw on, plus full case studies worked end to end with explicit trade-off reasoning.
Behavioural & Culture Fit21 questions · 8 topicsThe round candidates most often under-prepare, treated as a skill with structure rather than small talk, including the rubric interviewers score against.
HR, Screening & Offer18 questions · 6 topicsThe recruiter and HR rounds that gate the technical loop and decide your compensation, where most candidates improvise and lose money.

Latest for this track

hardScenarioDesignConcept

A customer reports seeing another company's records in your admin console. Walk me through the first hour, and then tell me what you change so this class of bug cannot happen again.

Treat it as a confirmed breach until proven otherwise: preserve logs, scope who saw what, and start the disclosure clock alongside the investigation. The cause is almost always tenant scoping living in application code, where one forgotten filter leaks everything - so move enforcement below the code, to row-level security.

Security And Multitenancy4 minsenior, lead, staff

hardConceptScenario

An asyncio call times out and you handle the TimeoutError, but the background task keeps running and mutates shared state a few seconds later. What happened, and how do you make the timeout actually stop the work?

A timeout requests cancellation; it does not stop code. CancelledError is raised at the task next await point, so a task inside blocking code, shielded, in an executor thread, or swallowing the exception keeps running and commits its side effects late. Fix by re-raising CancelledError, keeping blocking calls off the loop, and using asyncio.timeout with a TaskGroup.

Concurrency In Python4 minmid, senior, staff

hardConceptScenarioDesign

Your error budget burn alert pages every few hours, but half the time nobody outside the team has noticed anything. How do you tune it without simply making it quieter?

A single threshold on a single window cannot separate a real outage from a brief spike, so it either pages on noise or misses slow burns. Replace it with multi-window multi-burn-rate alerting: a fast rule that pages and a slow rule that files a ticket, each gated by a short window so a spike that already stopped does not keep paging.

Sre And Reliability5 minmid, senior, staff

hardConceptScenarioDesign

Engineers added a user_id label to a few metrics and the observability bill went up tenfold. Explain what actually happened, and how you would fix it without losing the ability to debug.

Every distinct combination of label values is a separate time series with its own index entry, so an unbounded label multiplies series count by the number of users rather than adding one dimension. Metrics are the wrong tool for per-entity questions: keep labels bounded, move per-user debugging to exemplar-linked traces, and guard against the next unbounded label.

Observability At Scale5 minmid, senior, staff

hardConceptScenarioDesign

A table has forty million rows, thirty-five million of them soft-deleted, and every query for active rows has got slower. Would a partial index help, and what would you have to be careful about?

A partial index only contains rows matching its predicate, so indexing WHERE deleted_at IS NULL stores five million entries instead of forty million - smaller, shallower, and far likelier to stay cached. The catch is the planner only uses it when it can prove the query predicate implies the index predicate, and it does nothing about dead rows still in the heap.

Indexing5 minmid, senior, staff

hardScenarioConceptDesign

Pods are being evicted during node pressure even though your CPU dashboards look fine. Where do you look, and what would you change so it stops?

CPU is compressible, so the kubelet throttles it rather than evicting for it. Eviction comes from memory, ephemeral storage or inodes - and which pods die is decided by QoS class, not by who caused the pressure. Check node conditions and eviction thresholds first, then fix the requests and limits so critical workloads are Guaranteed rather than BestEffort.

Kubernetes4 minmid, senior, staff