As a Software Engineer at Grab, you are at the heart of Southeast Asia’s leading superapp. You will be responsible for building, maintaining, and scaling the infrastructure that powers millions of daily transactions across ride-hailing, food delivery, and financial services. Your work directly impacts the daily lives of millions of users and partners, requiring a commitment to high-availability systems and user-centric problem solving. You will join a diverse engineering organization where technical excellence is balanced with the need for rapid iteration. Whether you are working on backend microservices, mobile application performance, or complex data architecture, you will be expected to think about scale, reliability, and security from day one. It is a fast-paced environment where you will collaborate with cross-functional teams to solve real-world problems that define the digital landscape of the region. ##### Tip While the interview process is generally structured, be prepared for variance depending on the specific team and region you are applying to. Some teams prioritize deep system design expertise, while others focus heavily on specific language proficiency or algorithmic rigor.
Recruiter Screening
reportedInitial assessment by a recruiter to evaluate your background and fit for the role.
What to demonstrate
- Initial assessment by a recruiter to evaluate your background and fit for the role
- Depth in Data Structures
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 Evaluation
reportedIn-depth technical assessment to evaluate your coding skills and problem-solving abilities.
What to demonstrate
- In-depth technical assessment to evaluate your coding skills and problem-solving abilities
- Depth in Data Structures
How to prepare
- Answer aloud and timed: Given a string of numbers, add them as strings.
- Answer aloud and timed: Discuss the time and space complexity of your solution.
Managerial Alignment
reportedDiscussion with the hiring manager to assess cultural fit and alignment with team goals.
What to demonstrate
- Discussion with the hiring manager to assess cultural fit and alignment with team goals
- Depth in Data Structures
How to prepare
- Answer aloud and timed: Optimize a brute-force approach to a more efficient algorithm using HashMaps or Two Pointers.
- Answer aloud and timed: Design a Rate Limiter for a high-traffic API.
1 candidate reports. Individual accounts describe a particular role and hiring cycle.
Grab Software Engineer interview with next-day HR screening
A recruiter reached out through LinkedIn, and things moved quickly. I scheduled the HR conversation and interviewed the following day. It was friendly and focused on my background, the engineering role I wanted, my resume, and a short self-introduction. A day or so later, I had a user-related round. The interviewer was accommodating and professional, asked clear questions, and kept a warm tone. T…
Read full experiencePracHub editorial advice for the preparation topics above.
Think Aloud
Your interviewer cares more about your thought process than the final line of code. Narrate your assumptions and trade-offs clearly.
Drive the Requirements
If a question seems ambiguous, ask clarifying questions before starting. This demonstrates your ability to gather requirements in a real work setting.
Study the Product
Familiarize yourself with the Grab ecosystem. Understanding the challenges of a ride-hailing or delivery platform will help you provide more relevant system design answers.
Prepare Your Projects
Have two or three projects ready to discuss in depth. Be prepared to explain the technical challenges you faced and how you overcame them.
Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.
Solve a Top K Frequent Elements problem.
Solve a Top K Frequent Elements problem.
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 algorithm to check for anagrams or string manipulation.
Implement an algorithm to check for anagrams or string manipulation.
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 string of numbers, add them as strings.
Given a string of numbers, add them as strings.
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?
Discuss the time and space complexity of your solution.
Discuss the time and space complexity of your solution.
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?
Optimize a brute-force approach to a more efficient algorithm using HashMaps or Two Pointers.
Optimize a brute-force approach to a more efficient algorithm using HashMaps or Two Pointers.
Approach
- Say what the runtime actually does before reasoning about the code.
- Name what is shared across threads and what owns each piece of state.
- Identify the window where an invariant is briefly untrue.
- Distinguish a value from a reference to it, and say which one you handed out.
Follow-up
- What happens if two callers reach this at the same time?
- Where could this allocate more than you expect?
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?
Write the update path that detects a concurrent edit
resource carries version INT NOT NULL DEFAULT 1. resource_revision holds revision_id, resource_id, version, actor_user_id, change_kind, patch JSONB, request_id, created_at with UNIQUE (resource_id, version). outbox_event holds aggregate_type, aggregate_id, aggregate_version, event_type, payload, status. A PUT carries the version the client read. Write the exact statements for the single transaction that applies the edit, records the revision and enqueues 'resource.updated', and give the handler's branch on zero affected rows. Then say what PostgreSQL 16 does under READ COMMITTED when two of these updates hit one row at once.
Approach
- One transaction, three writes, no network call inside it: UPDATE resource SET title = $3, version = version + 1, updated_at = now() WHERE resource_id = $1 AND tenant_id = $4 AND version = $2; then INSERT the resource_revision row at version $2 + 1; then INSERT the outbox_event row at the same aggregate_version. The event goes to a table rather than a broker because no transaction spans both.
- Branch on the affected-row count before doing anything else. Zero has three causes — stale version, wrong tenant, row gone — so re-read once and map to 409 carrying the current version, or 404 for an id outside the caller's tenant, which also stops the endpoint confirming that another tenant's id exists.
- State the engine behaviour instead of assuming it. Under READ COMMITTED the second UPDATE blocks on the row lock, and when the first commits PostgreSQL re-evaluates the WHERE clause against the newly committed row, so the version predicate now fails and the statement reports zero rows. Under REPEATABLE READ the identical collision raises SQLSTATE 40001 instead, so the handler must fold both shapes into one conflict response.
- Keep UNIQUE (resource_id, version) even though the predicate already serialises writers. It is what makes a lost update unwritable if any other path ever reaches the revision table, and it converts a logic bug into 23505 rather than into a silently missing history row.
Follow-up
- A client sends the version it read ten minutes ago and the resource has moved three versions. What is in your 409 so it can resolve the conflict without a full re-fetch?
- Two editors, two disjoint fields, no overlap. Does your answer still refuse the second write, and should it?
Design a Rate Limiter for a high-traffic API.
Design a Rate Limiter for a high-traffic API.
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?
Design a URL Shortener service.
Design a URL Shortener service.
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 Payment System for a bank, including database schema and service communication?
How would you design a Payment System for a bank, including database schema and service communication?
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?
Explain how you handle microservice authentication (e.g., mTLS vs. JWT).
Explain how you handle microservice authentication (e.g., mTLS vs. JWT).
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?
Edge instances grow 400 MB per hour until the nightly restart
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?
Built from the rounds and topics Grab candidates report.
Prepare, practise & reflect
One practical outcome each day. Spend longer where you need it.
0 / 7 done01Map the Grab loop
- Write out the reported sequence: Recruiter Screening, Technical Evaluation, Managerial Alignment.
- 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.
02Work Data Structures
- Spend the session on Data Structures, which Grab candidates report being tested on.
- Write one worked example in Data Structures and time yourself on it.
Deliverable: One timed worked example in Data Structures.
03Work System Design
- Spend the session on System Design, which Grab 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.
04Work Algorithms (Problem Solving)
- Spend the session on Algorithms (Problem Solving), which Grab candidates report being tested on.
- Write one worked example in Algorithms (Problem Solving) and time yourself on it.
Deliverable: One timed worked example in Algorithms (Problem Solving).
05Answer out loud: Coding and Algorithms
- Answer aloud, timed: Solve a Top K Frequent Elements problem.
- Answer aloud, timed: Implement an algorithm to check for anagrams or string manipulation.
Deliverable: Spoken answers to 2 reported Coding and Algorithms question(s), under time.
06Answer out loud: System Design and Architecture
- Answer aloud, timed: Design a Rate Limiter for a high-traffic API.
- Answer aloud, timed: Design a URL Shortener service.
Deliverable: Spoken answers to 2 reported System Design and Architecture question(s), under time.
07Answer out loud: Behavioral and Culture Fit
- Answer aloud, timed: Describe a time you had to deal with a significant technical constraint.
- Answer aloud, timed: How do you handle a disagreement with a product manager or peer regarding a technical solution?
Deliverable: Spoken answers to 2 reported Behavioral and Culture Fit 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.
How do you handle cache invalidation and data sharding in a user database?
How do you handle cache invalidation and data sharding in a user database?
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 time you had to deal with a significant technical constraint.
Describe a time you had to deal with a significant technical constraint.
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 handle a disagreement with a product manager or peer regarding a technical solution?
How do you handle a disagreement with a product manager or peer regarding a technical solution?
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 project where you had to make a trade-off between speed and technical debt.
Tell me about a project where you had to make a trade-off between speed and technical debt.
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 approach mentoring junior engineers or receiving critical feedback?
How do you approach mentoring junior engineers or receiving critical feedback?
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
How do you handle cache invalidation and data sharding in a user database?
- 02
Describe a time you had to deal with a significant technical constraint.
- 03
How do you handle a disagreement with a product manager or peer regarding a technical solution?
- 04
Tell me about a project where you had to make a trade-off between speed and technical debt.
How long does the entire process take?
While it can be as short as a few weeks, it is not uncommon for the process to span 1–2 months. Stay proactive with your recruiter regarding your timeline.
Grab Software Engineer candidate reports ↗Is a university degree mandatory?
While many candidates have relevant degrees, professional experience and a strong portfolio of complex projects are the primary indicators of success.
Grab Software Engineer candidate reports ↗How difficult are the coding rounds?
Most coding rounds are at an "Easy-Medium" level on common coding platforms. The focus is on your thought process and communication rather than solving "Hard" puzzles.
Grab Software Engineer candidate reports ↗Does Grab offer remote work?
Policies vary by location and team; ensure you clarify the specific regional expectations with your recruiter during the initial screening.
Grab Software Engineer candidate reports ↗What topics does Grab test in interviews?
Grab interviews most often cover Live Coding, System Design, Case Study Analysis, Stakeholder Management, and Data Structures and Algorithms (DSA). The exact emphasis depends on the specific role you apply for.
Grab 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.
- 01Grab 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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