Mlabs hires Software Engineers; this guide collects what candidates report about the process.
General Screening Interview
reportedA quick interview to align on your background, experience, and mutual expectations.
What to demonstrate
- A quick interview to align on your background, experience, and mutual expectations
- Depth in Technical problem-solving
How to prepare
- Answer aloud and timed: What system design decisions did you make in your last project, and why did you choose that specific architecture?
- Answer aloud and timed: How do you evaluate which technologies, frameworks, or libraries to use when starting a complex new software project?
Homework Assignment
reportedA comprehensive assignment with multiple tasks to test your hands-on problem-solving skills.
What to demonstrate
- A comprehensive assignment with multiple tasks to test your hands-on problem-solving skills
- Depth in Technical problem-solving
How to prepare
- Answer aloud and timed: Describe a situation where you had to optimize an existing system for performance. What trade-offs did you make during this process?
- Answer aloud and timed: How do you design APIs and data models to ensure high scalability and ease of integration for external teams?
Technical Coding Session
reportedLive technical evaluations and discussions with engineering leaders.
What to demonstrate
- Live technical evaluations and discussions with engineering leaders
- Depth in Technical problem-solving
How to prepare
- Answer aloud and timed: Walk me through a challenging project you delivered. What was the core problem, and how did you solve it?
- Answer aloud and timed: Describe a time when a project faced significant technical hurdles. What actions did you take to keep the deliverables on track?
Conversations with Leadership
reportedInformal discussions with senior leadership, including the CTO or CEO, focusing on technical approach and system design.
What to demonstrate
- Informal discussions with senior leadership
- Focusing on technical approach and system design
How to prepare
- Answer aloud and timed: Detail a specific technical solution you proffered in a previous role. What was the ultimate impact, and how did you measure its success?
- Answer aloud and timed: How do you approach breaking down a complex, multi-part programming task under a tight deadline?
PracHub editorial advice for the preparation topics above.
Master the STAR Method
When discussing your past projects with the CTO or CEO, structure your answers using the Situation, Task, Action, and Result framework. Focus heavily on the specific actions you took and the outcomes.
Over-Communicate During Setup
Since the process can occasionally experience coordination delays, proactively confirm your interview links and schedules ahead of time.
Focus on Code Quality in the Homework
Treat the take-home assignment as production-level code. Include clear documentation, clean commit histories, and comprehensive tests.
Be Ready to Defend Your Tech Stack
Don't just list technologies on your resume; be prepared to explain exactly why you chose them for your past projects and what the alternatives were.
Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.
Find overlapping job attempts and peak concurrency from lease records
A day of job_run history yields about 50,000,000 attempt records: (job_run_id, job_type, attempt, started_at, finished_at which is NULL when the worker died, lease_expires_at). Leases expire on a clock, so a job that outran its lease ran twice. Produce (a) every job_run_id whose attempts overlapped in wall-clock time and (b) the peak number of simultaneously running attempts per job_type with the minute it occurred. Target O(n log n). State how you treat a NULL finished_at and what clock skew does to your answer.
Approach
- Define the interval before sorting anything: an attempt occupies [started_at, COALESCE(finished_at, lease_expires_at)). finished_at is observed and lease_expires_at is only a promise, so every attempt without a finish contributes an estimate and the whole result is a lower bound on overlap rather than an exact count.
- For peak concurrency, sweep: emit 2n endpoints, sort by (timestamp, kind) with ends ordered before starts at equal timestamps, then walk the sequence maintaining a counter per job_type and record each type's maximum with its timestamp. O(n log n) dominated by the sort, O(n) space, or O(1) extra if the sort is external and the walk streams.
- For overlap detection, do not compare attempts pairwise. A single global sort by (job_run_id, started_at) gives both the grouping and the order; within a group, keep the maximum end seen so far and report an overlap exactly when the next start is less than that running maximum, which is one linear pass after the sort.
- Half-open intervals matter and are easy to get wrong: with closed intervals an attempt ending at the same millisecond another begins reads as concurrency two, and across 50,000,000 records that artefact swamps the real signal.
Follow-up
- A handler is not idempotent and you have found 400 overlapping jobs. Which of them actually caused damage, and what would you query to find out?
- Peak concurrency for one job_type is 4 against a configured cap of 4. Is the cap working, or is the data hiding attempts that never started?
Track a rolling failure rate per destination for circuit decisions
The egress service delivers about 1,500 webhooks per second across roughly 40,000 destinations, each call bounded by a 10 second timeout. Maintain, per destination, the failure rate over the trailing 60 seconds so a caller can ask before dispatch whether the circuit should open. Attempts arrive as (destination_id, finished_at_ms, outcome). Requirement: amortised O(1) per attempt, with total memory bounded by the destination count rather than by traffic. Give the structure, its exact memory, and the rule that stops a destination with three attempts from opening a circuit.
Approach
- Name the exact-deque version and then reject it as the default. Holding timestamps and advancing a tail pointer past anything older than now minus 60 seconds is a correct two-pointer window at amortised O(1) per attempt, but its memory tracks in-window traffic, so one destination in a retry storm holds hundreds of thousands of entries while thousands of quiet destinations hold none.
- Use a ring of 60 one-second buckets per destination, each bucket a pair of counters for attempts and failures. On an attempt, advance the ring by the elapsed whole seconds, zeroing at most min(elapsed, 60) buckets, then increment the head. That is amortised O(1) with a fixed footprint per destination.
- State the footprint: 60 buckets times two 4-byte counters is 480 bytes of payload per destination, so 40,000 destinations is roughly 20 to 25 MB with per-entry overhead, bounded by the catalogue rather than by the rate. The cost is granularity, since the oldest bucket ages out in whole seconds, which is far tighter than the decision needs.
- Require a minimum sample before the circuit may open. A destination with three attempts and three failures reads as 100 percent and is not evidence; a floor of roughly 20 attempts in the window makes the ratio meaningful, and below that floor use a run of consecutive failures as the trigger instead.
Follow-up
- The fleet is 30 instances and each sees roughly a thirtieth of a destination's traffic. Where does the rate actually live, and what does a per-instance answer get wrong?
- A destination answers in 9.5 seconds and succeeds. It is not failing but it is consuming your per-destination concurrency. What signal should open the circuit here?
Collapse a redelivered event batch into per-aggregate high-water marks
You drain a batch of up to 5,000,000 events, each (aggregate_id BIGINT, aggregate_version INT, event_type, payload). The log guarantees order within one aggregate only; the batch merges 64 partitions, and a relay failover has redelivered a range, so an older version for an aggregate can appear after a newer one. Given a map of last_applied_version per aggregate, produce the events worth applying, at most one per (aggregate_id, version), plus the count discarded. Target O(n) time. State the memory for 2,000,000 distinct aggregates and what you do when it does not fit.
Approach
- One pass, one hash map from aggregate_id to the highest version kept, and a discard counter. An event whose version is at or below last_applied_version for its aggregate is dropped without further work, which is the whole reason the event carries its version rather than a delta. O(n) expected time, O(d) space in distinct aggregates.
- Keep the maximum, never the last occurrence. The redelivered range means the final appearance of an aggregate in the batch can be an older version than one seen earlier in the same batch, so last-wins applies stale state over newer state and the projection regresses with no error anywhere.
- Cost the memory instead of calling it large: an 8-byte key plus a 4-byte version is 12 bytes of payload, and an open-addressed table held at a 0.7 load factor costs roughly 17 bytes per entry before per-slot metadata, so 2,000,000 aggregates is tens of megabytes in a native layout and several times that in a runtime that boxes both key and value.
- If the distinct set exceeds memory, partition on hash(aggregate_id) mod P and reduce each partition independently. Every event for one aggregate hashes to the same partition, so the per-partition result is exact and the merge is concatenation rather than a second reduction.
Follow-up
- The payload is a patch rather than a snapshot, so applying only the highest version loses the intermediate changes. What changes in your reduction?
- How do you detect that version 7 arrived while version 6 was never delivered, and what should the consumer do about the gap?
Denormalise tenant onto revisions and backfill it live
resource_revision (revision_id, resource_id, version, actor_user_id, change_kind, patch, request_id, created_at) has 400M rows and no tenant column; tenant_id lives only on resource. Two reads need it: a tenant-scoped audit feed ordered by created_at DESC, and an offboarding purge. Both join back to resource today. Justify adding tenant_id to resource_revision against those two reads, name the anomaly the copy introduces and the constraint that prevents it, then give the ordered migration for a live table taking 1.2k writes/second — the lock each step takes, how the backfill is batched, and where each step stops being reversible. PostgreSQL 16.
Approach
- Justify from the access path rather than from taste. Without the column, the audit feed either scans resource_revision by created_at and discards other tenants' rows, or resolves the tenant's resource_ids first and probes with them — both proportional to the tenant's whole history rather than to one page. With (tenant_id, created_at DESC, revision_id DESC) it is a seek that stops at 50 rows, and the purge becomes a ranged delete instead of a join.
- Name the cost exactly: a second copy of a fact can disagree with the first. Make the disagreement unwritable rather than documented — add UNIQUE (resource_id, tenant_id) on resource so it can serve as a foreign-key target, then FOREIGN KEY (resource_id, tenant_id) REFERENCES resource (resource_id, tenant_id) on the revision table. A revision can then only ever carry its parent's tenant.
- Step one, expand: ALTER TABLE resource_revision ADD COLUMN tenant_id BIGINT NULL, with no default, so it is a catalogue change and no rewrite. It still needs ACCESS EXCLUSIVE for an instant, and that instant queues behind the longest open transaction on the table while every later query queues behind it — set lock_timeout to 2s and retry rather than wait.
- Step two, dual-write: deploy the writer that populates tenant_id on every new revision while reads still use the join. Reversible by redeploying the previous build, because nothing reads the column yet.
Follow-up
- The backfill is half finished and a rollback is required. What state is the table in, and what does the previous build do with a half-populated column?
- How do you verify the backfill actually finished, given rows are still being inserted while it runs?
Replace offset paging on the resource feed with keyset
resource holds resource_id, tenant_id, owner_user_id, title, body_ref, version, status ('draft','active','archived','deleted'), created_at, updated_at, deleted_at, with an index on (tenant_id, status, updated_at DESC, resource_id DESC). The listing endpoint returns active resources for one tenant, newest update first, 50 per page, today with LIMIT 50 OFFSET n. Tenants reach page 400 and rows are created while they read. Write the keyset query, define what the cursor carries and how it is encoded, and say which part of the index each predicate uses. Assume PostgreSQL 16.
Approach
- Name the two failures separately. OFFSET 20000 makes the server produce and discard 20,000 rows, so page cost grows with depth rather than with page size. Independently, any write that changes how many rows sort above the offset moves the window between two fetches, and the direction decides which anomaly you get: an insert lands at the head of updated_at DESC and pushes already-returned rows down past the boundary, so they are returned a second time; a delete above the offset, or a row whose updated_at is bumped above the cursor, pulls rows up and one is never returned at all. Nothing in the response reveals either.
- Write the seek: WHERE tenant_id = $1 AND status = 'active' AND (updated_at, resource_id) < ($2, $3) ORDER BY updated_at DESC, resource_id DESC LIMIT 50. The row-value comparison is one index range rather than a disjunction, and both columns are NOT NULL, which is what makes that comparison well defined.
- Map each predicate onto the index: tenant_id and status are equality on the leading columns, (updated_at, resource_id) is the range, and the ORDER BY matches the index order so no Sort node appears and the scan stops after 50 rows. The DESC in the definition only matters for mixed directions — a plain ascending btree on the same columns is read backwards for this query.
- Put both sort columns in the cursor and nothing the client can tamper with into another tenant: base64 of (updated_at, resource_id), validated server-side, with tenant_id taken from the principal.
Follow-up
- The client asks for 'jump to page 400'. What do you offer instead, and what does the honest version cost?
- Sort order becomes user-selectable across four columns. How many indexes is that, and which would you refuse to add?
What system design decisions did you make in your last project, and why did you choose that specific architect
What system design decisions did you make in your last project, and why did you choose that specific 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 do you evaluate which technologies, frameworks, or libraries to use when starting a complex new software p
How do you evaluate which technologies, frameworks, or libraries to use when starting a complex new software project?
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?
Describe a situation where you had to optimize an existing system for performance. What trade-offs did you mak
Describe a situation where you had to optimize an existing system for performance. What trade-offs did you make during this process?
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 do you design APIs and data models to ensure high scalability and ease of integration for external teams?
How do you design APIs and data models to ensure high scalability and ease of integration for external teams?
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?
Walk me through a challenging project you delivered. What was the core problem, and how did you solve it?
Walk me through a challenging project you delivered. What was the core problem, and how did you solve it?
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?
Detail a specific technical solution you proffered in a previous role. What was the ultimate impact, and how d
Detail a specific technical solution you proffered in a previous role. What was the ultimate impact, and how did you measure its success?
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?
How do you approach breaking down a complex, multi-part programming task under a tight deadline?
How do you approach breaking down a complex, multi-part programming task under a tight deadline?
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?
Exports duplicate a row range about once a week
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?
Built from the rounds and topics Mlabs candidates report.
Prepare, practise & reflect
One practical outcome each day. Spend longer where you need it.
0 / 7 done01Map the Mlabs loop
- Write out the reported sequence: General Screening Interview, Homework Assignment, Technical Coding Session, Conversations with Leadership.
- 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 4 reported rounds, with the weakest marked.
02Work Technical problem-solving
- Spend the session on Technical problem-solving, which Mlabs 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.
03Work TypeScript
- Spend the session on TypeScript, which Mlabs candidates report being tested on.
- Write one worked example in TypeScript and time yourself on it.
Deliverable: One timed worked example in TypeScript.
04Work React
- Spend the session on React, which Mlabs candidates report being tested on.
- Write one worked example in React and time yourself on it.
Deliverable: One timed worked example in React.
05Answer out loud: System Design & Architecture
- Answer aloud, timed: What system design decisions did you make in your last project, and why did you choose that specific architecture?
- Answer aloud, timed: How do you evaluate which technologies, frameworks, or libraries to use when starting a complex new software project?
Deliverable: Spoken answers to 2 reported System Design & Architecture question(s), under time.
06Answer out loud: Project Experience & Problem-Solving
- Answer aloud, timed: Walk me through a challenging project you delivered. What was the core problem, and how did you solve it?
- Answer aloud, timed: Describe a time when a project faced significant technical hurdles. What actions did you take to keep the deliverables on track?
Deliverable: Spoken answers to 2 reported Project Experience & Problem-Solving question(s), under time.
07Answer out loud: Behavioral & Collaboration
- Answer aloud, timed: Tell me about a time you had to align with a stakeholder who had a different technical perspective than yours. How did you handle it?
- Answer aloud, timed: Describe a situation where you had to manage technical debt while still delivering new features on a tight schedule.
Deliverable: Spoken answers to 2 reported Behavioral & Collaboration question(s), under time.
Expand any day for tasks and deliverables. Your progress is saved on this device.
Behavioural rounds judge the decision you made and what it cost.
Describe a time when a project faced significant technical hurdles. What actions did you take to keep the deli
Describe a time when a project faced significant technical hurdles. What actions did you take to keep the deliverables on track?
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?
Tell me about a time you had to align with a stakeholder who had a different technical perspective than yours.
Tell me about a time you had to align with a stakeholder who had a different technical perspective than yours. How did you handle it?
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?
Describe a situation where you had to manage technical debt while still delivering new features on a tight sch
Describe a situation where you had to manage technical debt while still delivering new features on a tight schedule.
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?
How do you maintain clear and effective communication when working in a highly decentralized or remote team en
How do you maintain clear and effective communication when working in a highly decentralized or remote team environment?
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?
- 01
Describe a time when a project faced significant technical hurdles. What actions did you take to keep the deliverables on track?
- 02
Tell me about a time you had to align with a stakeholder who had a different technical perspective than yours. How did you handle it?
- 03
Describe a situation where you had to manage technical debt while still delivering new features on a tight schedule.
- 04
How do you maintain clear and effective communication when working in a highly decentralized or remote team environment?
How difficult is the Mlabs interview process?
The process is generally rated as average in difficulty, but it requires significant effort due to the comprehensive take-home "homework" assignment. Success depends on your practical coding skills and your ability to articulate system design trade-offs.
Mlabs Software Engineer candidate reports ↗What is the company culture like at Mlabs?
The culture is highly professional yet informal and laidback. Communication is direct, and there is a strong emphasis on technical excellence, autonomy, and taking ownership of your projects.
Mlabs Software Engineer candidate reports ↗How long does the take-home assignment usually take?
Candidates are typically given one week to complete the four tasks. While some tasks are highly interesting, they can be challenging, so it is recommended to budget several hours throughout the week to complete them thoroughly.
Mlabs Software Engineer candidate reports ↗What should I focus on during the executive interview with the CEO/CTO?
Focus on demonstrating a holistic understanding of your past projects. Be prepared to discuss not just the "how" (technologies and coding) but also the "why" (business goals, challenges faced, and strategic decisions).
Mlabs Software Engineer candidate reports ↗Are there remote work opportunities?
Yes, Mlabs operates globally and supports remote work configurations. However, maintaining clear asynchronous communication is vital to succeeding in this decentralized environment.
Mlabs Software Engineer candidate reports ↗What topics does Mlabs test in interviews?
Mlabs interviews most often cover Technical problem-solving, TypeScript, React, System design, and Full-stack engineering. The exact emphasis depends on the specific role you apply for.
Mlabs Software Engineer candidate reports ↗Sources & methodology 3 sources ↗
No official company page is cited. Rounds and questions come from candidate reports and PracHub editorial material; each source shows the date it was read.
- 01Mlabs Software Engineer candidate reports ↗
Company-reported rounds, questions and FAQ.
Candidate reports · Accessed 2026-09-22 - 02PracHub Software Engineer practice ↗
PracHub practice material, not company-reported.
PracHub page · Accessed 2026-09-22 - 03PracHub preparation framework ↗
PracHub preparation guidance.
PracHub page · Accessed 2026-09-22
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