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Integral Ad Science Software Engineer Interview Guide

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

The 60-second brief

As a Software Engineer at Integral Ad Science (IAS), you sit at the intersection of high-scale data processing and digital advertising integrity. Your work directly impacts how global brands ensure their advertisements appear in safe, suitable, and viewable environments. You are not just writing code; you are building the infrastructure that powers real-time decisioning for some of the world’s largest advertisers and publishers. This role is critical to the IAS mission of providing transparency in the advertising ecosystem. You will tackle complex challenges involving massive data throughput, distributed systems, and low-latency requirements. Whether you are working on Big Data pipelines, backend Java services, or infrastructure, your contributions ensure that the Integral Ad Science platform remains a global leader in ad verification and fraud protection.

This guide is scoped to a Software Engineer candidate at Integral Ad Science.

Focus areas

SQL
Technical problem solving
Linux

The Integral Ad Science interview process

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

Initial Screening Call

A call with a recruiter to evaluate your fit for the role.

What to demonstrate

A call with a recruiter to evaluate your fit for the role
Depth in SQL

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.

Online Coding Assessment

An assessment on platforms like HackerRank or Coderbyte to gauge fundamental programming skills.

What to demonstrate

An assessment on platforms like HackerRank or Coderbyte to gauge fundamental programming skills
Depth in SQL

How to prepare

Answer aloud and timed: Describe your experience with Java and how you manage memory in high-throughput applications.
Answer aloud and timed: What are the advantages and disadvantages of using a microservices architecture in a distributed environment?

Technical Interviews

Multiple rounds covering coding, system design, and behavioral questions.

What to demonstrate

Multiple rounds covering coding, system design, and behavioral questions
Depth in SQL

How to prepare

Answer aloud and timed: Can you explain how OOP concepts are applied to maintain large, scalable codebases?
Answer aloud and timed: Solve a medium-level problem involving sorting algorithms.

Where candidates lose points

Be Professional

Even if you experience technical difficulties or an interviewer is late, maintain your composure and professionalism. It reflects your ability to handle stressful situations.

Clarify Requirements

If a question seems ambiguous, ask clarifying questions before diving into the code. This is a standard expectation for senior engineering roles.

Show Your Work

Whether you are on a whiteboard or a shared document, narrate your thought process. Interviewers at IAS are as interested in how you think as they are in the final answer.

Prepare for Rescheduling

Given the global nature of the company, it is not uncommon for interviews to be rescheduled. Be flexible and communicate your availability clearly.

Questions & practice

Solve a medium-level problem involving sorting algorithms.

Medium · Coding and 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?

Write a function to process a specific data structure and return a filtered result.

Medium · Coding and 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 set of requirements, how would you structure the classes and interfaces to ensure extensibility?

Medium · Coding and 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?

How would you optimize a SQL query that is performing poorly on a large dataset?

Medium · Technical and Domain Knowledge

Approach

Name the grain you start from and join outward from it.
Check whether any join is one-to-many before aggregating, or the sums inflate.
Say which index the query would use, and what makes it unusable.
Handle the rows that do not match: that is usually the actual question.

Follow-up

How does the query change if that join becomes one-to-many?
What happens to this when the table is ten times larger?

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?

Explain the difference between a batch processing system and a streaming system.

Medium · Technical and Domain Knowledge

Approach

Clarify what is being asked and what a complete answer contains.
State your assumptions explicitly before working the problem.
Say what you would check first and why it is the highest-information step.
Work from the requirement backwards to the design.

Follow-up

What assumption would you test first?
How would you know your answer was wrong?

What are the advantages and disadvantages of using a microservices architecture in a distributed environment?

Medium · Technical and Domain Knowledge

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?

Can you explain how OOP concepts are applied to maintain large, scalable codebases?

Medium · Technical and Domain Knowledge

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?

Implement an API endpoint that handles concurrent requests efficiently.

Medium · Coding and Problem-Solving

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?

Debug a provided snippet of code and explain your thought process.

Medium · Coding and Problem-Solving

Approach

Establish what changed and when, before forming any theory.
Pick a bisection that eliminates candidates whichever way it turns out.
Check the instrumentation before believing the symptom.
Separate the trigger from the cause; the deploy is rarely the bug.

Follow-up

What would you look at first, and what would it rule out?
How would you tell a cause from a coincidence here?

Suggested practice checklist

Built from the rounds and topics Integral Ad Science candidates report.

Day 1 — Map the Integral Ad Science loop

Write out the reported sequence: Initial Screening Call, Online Coding Assessment, Technical Interviews.
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 SQL

Spend the session on SQL, which Integral Ad Science candidates report being tested on.
Write one worked example in SQL and time yourself on it.

Deliverable: One timed worked example in SQL.

Day 3 — Work Technical problem solving

Spend the session on Technical problem solving, which Integral Ad Science candidates report being tested on.
Write one worked example in Technical problem solving and time yourself on it.

Deliverable: One timed worked example in Technical problem solving.

Day 4 — Work Linux

Spend the session on Linux, which Integral Ad Science candidates report being tested on.
Write one worked example in Linux and time yourself on it.

Deliverable: One timed worked example in Linux.

Day 5 — Answer out loud: Technical and Domain Knowledge

Answer aloud, timed: Explain the difference between a batch processing system and a streaming system.
Answer aloud, timed: How would you optimize a SQL query that is performing poorly on a large dataset?

Deliverable: Spoken answers to 2 reported Technical and Domain Knowledge question(s), under time.

Day 6 — Answer out loud: Coding and Problem-Solving

Answer aloud, timed: Solve a medium-level problem involving sorting algorithms.
Answer aloud, timed: Write a function to process a specific data structure and return a filtered result.

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

Day 7 — Answer out loud: Behavioral and Leadership

Answer aloud, timed: Tell me about a time you had to explain a complex technical trade-off to a non-technical stakeholder.
Answer aloud, timed: Describe a situation where you had to work with a team member who had a different working style than yours.

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.

Describe your experience with Java and how you manage memory in high-throughput applications.

Medium · Technical and Domain Knowledge

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?