Interview guides · Criteo

Criteo Software Engineer Interview Guide

Criteo Software Engineer interview guide: reported rounds, questions and how candidates describe the process.

The 60-second brief

As a Software Engineer at Criteo, you are at the heart of one of the world’s most sophisticated advertising technology platforms. Your work directly influences how billions of personalized ads are served in real-time, requiring you to engineer solutions that handle immense scale, sub-millisecond latency, and complex data processing. You will be building the infrastructure and algorithms that power global e-commerce, ensuring that our machine learning models and delivery systems operate with extreme efficiency and reliability. This role is not just about writing code; it is about solving "hard engineering" problems in a fast-paced environment. You will collaborate with cross-functional teams to design architectures that manage massive datasets, optimize ad-tech bidding strategies, and ensure the seamless integration of our services across global markets.

This guide is scoped to a Software Engineer candidate at Criteo.

Focus areas

System Design
Data Structures (DS)
Full-Stack Development

The Criteo interview process

Criteo candidates report 3 rounds over 3-5 weeks. The stages below are what candidates describe, not a published process.

Initial Screening

The process begins with an initial screening to assess your fit for the role.

What to demonstrate

The process begins with an initial screening to assess your fit for the role
Depth in System Design

How to prepare

Be able to walk your CV end to end in two minutes, and say why this company specifically.
Have your salary expectations, notice period and location constraints ready, and ask for the rest of the loop in writing.

Technical Evaluations

In-depth technical evaluations follow, focusing on your technical skills and problem-solving abilities.

What to demonstrate

In-depth technical evaluations follow
Focusing on your technical skills and problem-solving abilities

How to prepare

Answer aloud and timed: Describe an algorithm to perform a flood fill operation on a grid.
Answer aloud and timed: How would you design a search function within an array and then optimize its complexity?

Leadership Discussions

Final discussions with leadership to evaluate cultural alignment and team fit.

What to demonstrate

Final discussions with leadership to evaluate cultural alignment and team fit
Depth in System Design

How to prepare

Answer aloud and timed: Given a large dataset that exceeds RAM, how would you approach processing it?
Answer aloud and timed: How would you design a real-time chat application?

Where candidates lose points

Clarify early

When presented with an ambiguous problem, always ask clarifying questions to define the scope before you start coding.

Talk it out

Avoid long periods of silence. Your interviewer wants to hear your logic, as it helps them guide you if you hit a wall.

Review your resume

Be prepared to discuss any project on your resume in deep detail, including the technologies used and the specific challenges you faced.

Stay positive

Even if you struggle with a question, maintain a professional and collaborative attitude. Interviewers are often looking for how you respond to difficulty.

Questions & practice

How would you find a specific element in a sorted and rotated array?

Medium · Algorithmic Problem Solving

Approach

Restate the input: its shape, its size, and what is guaranteed about it.
Name the brute-force solution and its complexity before improving on it.
Choose the data structure from the access pattern, not from familiarity.
State the target complexity and say which constraint rules the naive version out.

Follow-up

How does this change if the input no longer fits in memory?
What is the worst case, and how likely is it on real data?

Can you implement a function to check if parentheses in a string are balanced?

Medium · Algorithmic Problem Solving

Approach

Restate the input: its shape, its size, and what is guaranteed about it.
Name the brute-force solution and its complexity before improving on it.
Choose the data structure from the access pattern, not from familiarity.
State the target complexity and say which constraint rules the naive version out.

Follow-up

How does this change if the input no longer fits in memory?
What is the worst case, and how likely is it on real data?

Describe an algorithm to perform a flood fill operation on a grid.

Medium · Algorithmic Problem Solving

Approach

Restate the input: its shape, its size, and what is guaranteed about it.
Name the brute-force solution and its complexity before improving on it.
Choose the data structure from the access pattern, not from familiarity.
State the target complexity and say which constraint rules the naive version out.

Follow-up

How does this change if the input no longer fits in memory?
What is the worst case, and how likely is it on real data?

Given a large dataset that exceeds RAM, how would you approach processing it?

Medium · Algorithmic Problem Solving

Approach

Restate the input: its shape, its size, and what is guaranteed about it.
Name the brute-force solution and its complexity before improving on it.
Choose the data structure from the access pattern, not from familiarity.
State the target complexity and say which constraint rules the naive version out.

Follow-up

How does this change if the input no longer fits in memory?
What is the worst case, and how likely is it on real data?

Keep soft-deleted accounts from blocking re-registration

Easy · soft delete · unique constraints · engine differences

app_user holds user_id, tenant_id, email CITEXT, password_hash (NULL for SSO principals), email_verified_at, auth_version, status ('invited','active','suspended','deactivated'), created_at, updated_at, deleted_at. Two live accounts for one address inside a tenant must be impossible, but an address freed by a soft delete must be reusable, and the same tenant may delete and re-register it repeatedly. Write the uniqueness DDL for PostgreSQL 16, then the equivalent for MySQL 8 where partial indexes do not exist, and say what each permits once three deleted rows already hold that address.

Approach

Start from what is actually unique: not (tenant_id, email), but (tenant_id, email) among live rows. PostgreSQL says that directly — CREATE UNIQUE INDEX app_user_live_email ON app_user (tenant_id, email) WHERE deleted_at IS NULL. A full constraint over the same two columns burns the address permanently the first time someone deletes an account.
Keep case-insensitivity in the type or the index, never in the application: CITEXT as given, or UNIQUE (tenant_id, lower(email)) as an expression index where the extension is unavailable. A case-sensitive unique column is exactly how two accounts for one human appear.
For MySQL 8 the predicate has to move inside the key: add a discriminator column that is a constant 0 while the row is live and is set to user_id on delete, with UNIQUE (tenant_id, email, deleted_marker). Live rows share the constant and still collide; deleted rows differ from each other and stop colliding.
State the NULL variant and its dependency: leaving the marker NULL for deleted rows also works, because a unique index treats NULLs as distinct — true in MySQL, and true in PostgreSQL only under the default NULLS DISTINCT, which PostgreSQL 15 lets you reverse. Check the polarity against the three existing deleted rows: constant-on-live is what preserves the collision you want, and reversing it silently admits duplicate live accounts.

Follow-up

A deleted account re-registers with the same address the next day. Do the old resource rows follow the new user_id, and how does the API keep the two principals apart?
How do you honour an erasure request while resource_revision.actor_user_id still references this table?

Hold a per-tenant active cap against concurrent creates

Hard · write skew · isolation levels · constraints

A tenant on the standard plan may hold at most 50 resources with status='active'. The create handler runs SELECT count(*) FROM resource WHERE tenant_id = $1 AND status = 'active', compares to 50, then inserts. Two creates arrive 3 ms apart on different instances and the tenant lands at 51. Name the anomaly, say whether PostgreSQL 16 READ COMMITTED or REPEATABLE READ prevents it and why, then give an implementation that holds the cap at READ COMMITTED with the exact statements. Finally, say what changes when the cap is 'at most one running export per tenant' on job_run.

Approach

Name it: write skew. The two transactions read an overlapping set and write disjoint rows, so there is no row-level conflict for the engine to detect and each commit is individually legal.
Rule out the levels precisely. READ COMMITTED takes a fresh snapshot per statement and takes no lock on the counted rows, so both see 49. PostgreSQL's REPEATABLE READ is snapshot isolation: it removes non-repeatable reads and phantoms within the snapshot but still admits write skew, because the anomaly is not a re-read of a changed row, it is a read of a set that a concurrent transaction invalidates. Only SERIALIZABLE closes it, by tracking the read dependency and aborting one transaction with SQLSTATE 40001 — a guarantee that exists only if the application re-runs the whole transaction from the read.
Convert the set predicate into a single-row conflict: keep tenant.active_resource_count and run UPDATE tenant SET active_resource_count = active_resource_count + 1 WHERE tenant_id = $1 AND active_resource_count < 50 in the same transaction as the INSERT. Zero affected rows is the cap, returned as 409. The row lock serialises the decision at any isolation level, and contention is bounded to one tenant's row — which is also the fair-scheduling unit, unlike a global counter that would convoy every tenant behind one row.
State the cost you just took on: a counter is a second source of truth that can drift, so every path that changes status must adjust it inside the same transaction, and a periodic reconciliation has to exist, with resource_revision as the authority for what the count should have been.

Follow-up

A resource moves from archived back to active. Which statements change, and what breaks if the counter update and the status change land in different transactions?
The cap becomes plan-dependent and a plan can change mid-month. Where does the number 50 live, and who reads it?

How would you design a search function within an array and then optimize its complexity?

Medium · Algorithmic Problem Solving

Approach

Fix the scope first: who calls this, how often, and what they do when it fails.
Name the read and write paths separately; they rarely have the same bottleneck.
Choose a partition key and say what query it makes expensive.
State the consistency you need, and where you are willing to be stale.

Follow-up

What breaks first when traffic grows ten times?
How does this behave when that dependency is down for an hour?

How would you design a real-time chat application?

Medium · System Design and Architecture

Approach

Fix the scope first: who calls this, how often, and what they do when it fails.
Name the read and write paths separately; they rarely have the same bottleneck.
Choose a partition key and say what query it makes expensive.
State the consistency you need, and where you are willing to be stale.

Follow-up

What breaks first when traffic grows ten times?
How does this behave when that dependency is down for an hour?

Design a system for an RSS feed that handles high traffic.

Medium · System Design and Architecture

Approach

Fix the scope first: who calls this, how often, and what they do when it fails.
Name the read and write paths separately; they rarely have the same bottleneck.
Choose a partition key and say what query it makes expensive.
State the consistency you need, and where you are willing to be stale.

Follow-up

What breaks first when traffic grows ten times?
How does this behave when that dependency is down for an hour?

How would you architect a solution to distribute load across multiple hosts?

Medium · System Design and Architecture

Approach

Fix the scope first: who calls this, how often, and what they do when it fails.
Name the read and write paths separately; they rarely have the same bottleneck.
Choose a partition key and say what query it makes expensive.
State the consistency you need, and where you are willing to be stale.

Follow-up

What breaks first when traffic grows ten times?
How does this behave when that dependency is down for an hour?

What are the considerations for designing a REST API that manages campaigns and products?

Medium · System Design and Architecture

Approach

Say who the caller is and what they do when the call fails halfway.
Define the identity of a request so a retry cannot double-apply it.
Separate accepted, pending, failed and confirmed; they are different facts.
Design the error taxonomy before the success shape; callers branch on it.

Follow-up

What happens if the caller retries after a timeout?
How does a client discover it is on an old version of this contract?

Edge instances grow 400 MB per hour until the nightly restart

Hard · memory leak · heap analysis · metric cardinality

Edge API instances start at 700 MB resident and grow about 400 MB/hour; a nightly rolling restart has hidden it for weeks. Growth continues unchanged when request rate halves overnight, p99 degrades in the last hours before an instance is recycled, and heap used immediately after a forced full GC rises monotonically. The service holds no product state. Name the discriminating measurement that separates the plausible causes, give the most likely cause, and give the fix and how you would verify it.

Approach

Separate resident memory from live heap first, because they fail differently. Resident size can grow from fragmentation, native buffers or thread stacks while the heap is flat; heap used after a full GC rising monotonically is the measurement that says objects are reachable and not being released. You already have it, so this is retention, not fragmentation, and that closes off half the candidate list.
Use the rate's independence from traffic as the discriminator. Growth that continues at half the request rate rules out per-request objects that are merely slow to collect and points at a structure that grows with distinct values observed rather than with call volume. Write the candidates that have that property: a metrics registry keyed on a high-cardinality label, an unevicted cache, an interner, a per-key lock map.
Take two heap snapshots an hour apart and diff by retained size, reading the dominator tree, not by allocation count or instance count. Expect one root holding a map with millions of entries, then follow the reference chain to the code that inserts and never removes. Allocation profilers point at churn, which is the wrong signal here.
The candidate that fits this service is an observability label carrying an identifier, such as a request path recorded before templating so that /v1/resources/48213 becomes its own metric series. That grows with distinct ids seen, is independent of rate, and explains the late p99 degradation, since GC cost rises with the size of the live set.

Follow-up

Post-GC heap is now flat but resident size still creeps. What are you looking at, and does it matter?
How would you have detected this before an OOM, given the nightly restart masked the trend?

Suggested practice checklist

Built from the rounds and topics Criteo candidates report.

Day 1 — Map the Criteo loop

Write out the reported sequence: Initial Screening, Technical Evaluations, Leadership Discussions.
For each round, write one sentence on what it is judging, from the description above, and mark the one you are least ready for.

Deliverable: A one-page map of the 3 reported rounds, with the weakest marked.

Day 2 — Work System Design

Spend the session on System Design, which Criteo candidates report being tested on.
Write one worked example in System Design and time yourself on it.

Deliverable: One timed worked example in System Design.

Day 3 — Work Data Structures (DS)

Spend the session on Data Structures (DS), which Criteo candidates report being tested on.
Write one worked example in Data Structures (DS) and time yourself on it.

Deliverable: One timed worked example in Data Structures (DS).

Day 4 — Work Full-Stack Development

Spend the session on Full-Stack Development, which Criteo candidates report being tested on.
Write one worked example in Full-Stack Development and time yourself on it.

Deliverable: One timed worked example in Full-Stack Development.

Day 5 — Answer out loud: Algorithmic Problem Solving

Answer aloud, timed: How would you find a specific element in a sorted and rotated array?
Answer aloud, timed: Can you implement a function to check if parentheses in a string are balanced?

Deliverable: Spoken answers to 2 reported Algorithmic Problem Solving question(s), under time.

Day 6 — Answer out loud: System Design and Architecture

Answer aloud, timed: How would you design a real-time chat application?
Answer aloud, timed: Design a system for an RSS feed that handles high traffic.

Deliverable: Spoken answers to 2 reported System Design and Architecture question(s), under time.

Day 7 — Answer out loud: Behavioral and Leadership

Answer aloud, timed: Tell me about a time you had a technical disagreement with a colleague. How did you resolve it?
Answer aloud, timed: Why are you interested in working at Criteo specifically?

Deliverable: Spoken answers to 2 reported Behavioral and Leadership question(s), under time.

Behavioural preparation

Behavioural rounds judge the decision you made and what it cost.

How do you handle data consistency in a distributed advertising system?

Medium · System Design and Architecture

Approach

Pick a story where you made the decision, not one where you watched it.
State the situation in two sentences and spend the rest on the reasoning.
Give the blast radius: what could have broken, and what you measured.
Name the disagreement and how you resolved it with evidence.

Follow-up

What would you do differently if you ran that again?
How did you know your change caused the improvement?