Interview guides · Interactive MP

Interactive MP Software Engineer Interview Guide

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

The 60-second brief

As a Software Engineer at Interactive MP, you are at the core of delivering high-performance, engaging digital experiences. This role is critical to maintaining the platform's reliability and scalability, ensuring that millions of users have a seamless, real-time experience during their workouts. Your work directly impacts how data is processed, how systems scale, and how new features are brought to life for a global community. You will contribute to complex problem spaces ranging from real-time data streaming and hardware integration to building robust, user-facing applications. The work is fast-paced and requires a balance of technical rigor and creative problem-solving.

This guide is scoped to a Software Engineer candidate at Interactive MP.

Focus areas

System Design
React
Coding Round Skills

The Interactive MP interview process

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

Recruiter Screen

Initial contact with a recruiter to discuss the candidate's background and fit for the role.

What to demonstrate

Initial contact with a recruiter to discuss the candidate's background and 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 Assessment

Evaluation that may include take-home tasks or live coding to assess technical skills.

What to demonstrate

Evaluation that may include take-home tasks or live coding to assess technical skills
Depth in System Design

How to prepare

Answer aloud and timed: Build a single-view app that displays the system time.
Answer aloud and timed: Implement an LRU cache.

Panel/Onsite Round

Final round of interviews that includes multiple interviewers assessing technical depth and team fit.

What to demonstrate

Final round of interviews that includes multiple interviewers assessing technical depth and team fit
Depth in System Design

How to prepare

Answer aloud and timed: Extract information using operations on a list.
Answer aloud and timed: Design the architecture for a class-ranking system.

Where candidates lose points

Clarify the requirements

Before diving into any coding problem, ask clarifying questions to ensure you understand the constraints and expectations.

Explain your trade-offs

When designing a system, always explain why you chose one solution over another. There is rarely one "perfect" answer; showing you understand the pros and cons is a sign of seniority.

Leverage your projects

Use your previous work as a narrative anchor. Be ready to discuss the technical challenges you faced, how you solved them, and what you learned.

Questions & practice

Implement a list-based data structure that performs inserts and deletes in constant time.

Medium · Technical and Coding Proficiency

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?

Find the bug in a code base and implement a new feature.

Medium · Technical and Coding Proficiency

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?

Build a single-view app that displays the system time.

Medium · Technical and Coding Proficiency

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?

Implement an LRU cache.

Medium · Technical and Coding Proficiency

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?

Extract information using operations on a list.

Medium · Technical and Coding Proficiency

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?

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?

Stop tag and share joins from fanning out a page

Medium · join fan-out · semi-joins · many-to-many

resource_tag is (resource_id, tag_id) with PK (resource_id, tag_id); resource_share is (resource_id, shared_with_user_id, permission). The tagged-and-shared listing inner-joins resource to both, filters tenant_id, tag_id = ANY($2) and shared_with_user_id = $3, orders by updated_at DESC and takes 50. Pages come back with fewer than 50 distinct resources and the total in the header is far too high. Explain the row multiplication, rewrite both the page query and the count query so each is correct, and name the index each one needs. PostgreSQL 16.

Approach

Do the arithmetic against the predicates that are actually there. An inner join emits one row per matching child row, and both joins are filtered: tag_id = ANY($2) admits only the requested tags, shared_with_user_id = $3 admits one user's share rows. So a resource holding three of the requested tags and shared with $3 once yields three rows, not one — the multiplier is its count of matching tags times its share rows for that single user, and that second factor is 1 unless the table admits duplicate (resource_id, shared_with_user_id) pairs. LIMIT 50 then limits rows rather than resources, and COUNT(*) counts pairs — the header is the product, not the population.
Reject DISTINCT as the fix. It deduplicates after the product has been built, so the planner must materialise and sort the fanned-out set before the LIMIT can apply, and it leaves any SUM or AVG in the same select list wrong.
Rewrite both filters as semi-joins, keeping resource as the only row source: AND EXISTS (SELECT 1 FROM resource_tag rt WHERE rt.resource_id = r.resource_id AND rt.tag_id = ANY($2)) and the same shape against resource_share. A semi-join stops at the first match per resource and preserves the driving index order, so ORDER BY updated_at DESC, resource_id DESC LIMIT 50 still stops after 50 rows.
Count with the same predicates and no join at all: SELECT count(*) FROM resource r WHERE r.tenant_id = $1 AND r.status = 'active' AND EXISTS (...) AND EXISTS (...). Nothing multiplies a resource, so the number is the population.

Follow-up

The filter changes from 'any of these tags' to 'all of these tags'. Rewrite it and state what it costs relative to the ANY form.
A resource can be shared with the same user twice under different permissions. Does your count change, and should it?

Design the architecture for a class-ranking system.

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 game app logic for a rewarding system.

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 design and implement a password checker given specific requirements?

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?

Exports duplicate a row range about once a week

Hard · leases · clock skew · idempotency

Roughly once a week an export writes a file containing a duplicated range of rows. The affected job_run rows show attempt = 1, status = succeeded, one started_at, and a lease_owner naming a different host from the one whose logs show the job starting. Leases last 30 seconds and are heartbeated every 10 from inside the handler; lease_expires_at is computed on the worker and compared against the database's now(). Find the mechanism, and give a fix that holds even if you cannot fix the clocks.

Approach

Start from the fact that eliminates the obvious answer. attempt = 1 means no retry was recorded, so this is not a re-run after failure; two workers ran the same row concurrently and the takeover path never touched the counter. lease_owner naming a host other than the one that started the job is the same statement from the other side.
Enumerate the mechanisms that cause a premature takeover, then find the signal that separates them. Either the lease genuinely expired because the heartbeat did not fire, which is what happens when the heartbeat runs on the handler's own thread and the handler makes a long blocking call, or it only appeared expired because two clocks disagree, since lease_expires_at is written from the worker's clock and evaluated against the database's. The discriminator is the distribution: incidents clustered on the longest exports indict the heartbeat, incidents clustered on one host indict skew. Measure both, and measure each host's offset against the database directly.
Read the reclaim query precisely. In PostgreSQL now() is transaction start time, not statement time, so a reclaimer holding a long transaction compares against an older timestamp than expected; clock_timestamp() is the statement-time function. This is worth ruling in or out before you redesign anything, because it changes which rows look expired.
Remove the second clock rather than trying to synchronise it. Issue and extend the lease in the database, with lease_expires_at = now() + interval '30 seconds' in both the claim and the heartbeat, so exactly one clock is ever compared and worker skew stops mattering to this predicate.

Follow-up

The displaced worker has already streamed half the file to object storage. What makes that side effect safe to repeat?
You now count takeovers. What alert fires on that counter, and at what threshold?

Suggested practice checklist

Built from the rounds and topics Interactive MP candidates report.

Day 1 — Map the Interactive MP loop

Write out the reported sequence: Recruiter Screen, Technical Assessment, Panel/Onsite Round.
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 Interactive MP 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 React

Spend the session on React, which Interactive MP candidates report being tested on.
Write one worked example in React and time yourself on it.

Deliverable: One timed worked example in React.

Day 4 — Work Coding Round Skills

Spend the session on Coding Round Skills, which Interactive MP candidates report being tested on.
Write one worked example in Coding Round Skills and time yourself on it.

Deliverable: One timed worked example in Coding Round Skills.

Day 5 — Answer out loud: Technical and Coding Proficiency

Answer aloud, timed: Implement a list-based data structure that performs inserts and deletes in constant time.
Answer aloud, timed: Find the bug in a code base and implement a new feature.

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

Day 6 — Answer out loud: System Design and Architecture

Answer aloud, timed: Design the architecture for a class-ranking system.
Answer aloud, timed: Design game app logic for a rewarding system.

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 faced adversity and how you overcame it.
Answer aloud, timed: Describe a successful test program you led or contributed to.

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 a time you made a design decision and explain the trade-offs.

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?