As a Software Engineer at Glance, you are at the forefront of redefining the mobile lock screen experience. Your work directly impacts millions of users by delivering personalized, high-quality content directly to their devices. You will be tasked with solving complex engineering challenges, ranging from optimizing web performance on constrained, low-end hardware to implementing robust caching mechanisms and efficient data structures. This role is critical to the Glance mission of creating seamless, engaging discovery experiences. You will operate in a fast-paced environment where your technical decisions—such as architectural choices for Android or system design strategies—directly influence product stability and user retention. It is a high-impact position that requires a blend of deep technical proficiency, architectural foresight, and a user-centric mindset. ##### Tip Given the scale of Glance, interviewers place a high premium on performance optimization. Be prepared to discuss how your code performs under memory and network constraints, as this is a recurring theme in technical evaluations.
Technical Rounds
reportedCandidates undergo a series of technical rounds focusing on core algorithmic problem-solving and domain-specific challenges.
What to demonstrate
- Candidates undergo a series of technical rounds focusing on core algorithmic problem-solving and domain-specific challenges
- Depth in System Design
How to prepare
- Answer aloud and timed: Implement a solution for a complex data structure problem (Medium/Hard level).
- Answer aloud and timed: Optimize an algorithm to reduce time complexity in a resource-constrained environment.
Managerial Assessment
reportedA final assessment to evaluate managerial skills and cultural fit within the team.
What to demonstrate
- A final assessment to evaluate managerial skills and cultural fit within the team
- Depth in System Design
How to prepare
- Answer aloud and timed: Solve a problem involving custom data structures to improve lookup speeds.
- Answer aloud and timed: Explain the trade-offs between different algorithmic approaches for a specific set of inputs.
PracHub editorial advice for the preparation topics above.
Think Aloud
During coding rounds, your thought process is just as important as the final output. Explain your trade-offs clearly.
Know Your Resume
Be prepared to dive deep into any project listed. Interviewers will ask about your specific contributions and the technical challenges you faced.
Focus on the "Why
Don't just provide the "right" answer. Explain why that specific approach was chosen over alternatives, especially regarding performance.
If you encounter a delay in feedback, do not hesitate to reach out to your HR contact once every 7–10 days
Maintain a professional tone, but be consistent in your follow-up.
Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.
Implement a solution for a complex data structure problem (Medium/Hard level).
Implement a solution for a complex data structure problem (Medium/Hard level).
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 an algorithm to reduce time complexity in a resource-constrained environment.
Optimize an algorithm to reduce time complexity in a resource-constrained environment.
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?
Solve a problem involving custom data structures to improve lookup speeds.
Solve a problem involving custom data structures to improve lookup speeds.
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?
Explain the trade-offs between different algorithmic approaches for a specific set of inputs.
Explain the trade-offs between different algorithmic approaches for a specific set of inputs.
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
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?
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?
Explain your approach to Clean Architecture in Android development.
Explain your approach to Clean Architecture in Android development.
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 the lifecycle and best practices for using Coroutines in a production app.
Describe the lifecycle and best practices for using Coroutines in a production app.
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 ensure high performance and smooth UI rendering on low-end devices?
How do you ensure high performance and smooth UI rendering on low-end devices?
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 content delivery that accounts for low-end device limitations.
Design a system for content delivery that accounts for low-end device limitations.
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 backend service to handle high-frequency content updates?
How would you architect a backend service to handle high-frequency content updates?
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?
Discuss strategies for minimizing battery and data consumption in a background-heavy application.
Discuss strategies for minimizing battery and data consumption in a background-heavy application.
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?
One customer endpoint stalls deliveries to every other destination
The egress service delivers about 1.5k webhooks/second across 40,000 destinations, with a per-destination concurrency cap of 4 and a 10-second connect-plus-read timeout. Throughput falls to 300/second, queue depth climbs, and p99 delivery latency for unaffected destinations goes from 200 ms to minutes, while the error rate barely moves. One tenant holds 900 destination rows whose URLs share a hostname that now answers in 9.5 seconds. Explain the mechanism with the arithmetic, then give the containment in the order you would apply it.
Approach
- Look at saturation before errors. A flat error rate with collapsing throughput says nothing is failing, things are waiting, so the first signal to pull is in-flight request count or pool wait time rather than the error counter. This is the distinction that decides the whole investigation.
- Group in-flight work by resolved host, not by destination id. The cap is keyed per destination row, so 900 rows sharing one hostname buy 3,600 concurrent slots against a single host, each held for 9.5 seconds. The bulkhead was never a bulkhead for that host, and grouping by the wrong dimension is why the dashboard looked healthy.
- Do the arithmetic in both directions. Required concurrency is arrival rate times latency, so 1.5k/second at 200 ms needs about 300 in flight, which is entirely consumed by 3,600 slow slots; conversely whatever concurrency is left sustains rate equals concurrency divided by 9.5 seconds, which is the 300/second you are seeing. Matching both numbers is what promotes this from a plausible story to the mechanism.
- Explain why the circuit breaker never helped. It opens on consecutive failures, and a 9.5-second response inside a 10-second timeout is a success. Slow is not failing, so an error-rate breaker cannot see this; you need a slow-call ratio, a deadline propagated from the caller's remaining budget, or a concurrency limiter.
Follow-up
- The host recovers to 80 ms. How long does the queue take to drain, and what does the drain do to the recovered host?
- Where should the 10-second timeout number actually come from?
Built from the rounds and topics Glance candidates report.
Prepare, practise & reflect
One practical outcome each day. Spend longer where you need it.
0 / 7 done01Map the Glance loop
- Write out the reported sequence: Technical Rounds, Managerial Assessment.
- 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 2 reported rounds, with the weakest marked.
02Work System Design
- Spend the session on System Design, which Glance 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.
03Work Web Performance Optimization
- Spend the session on Web Performance Optimization, which Glance candidates report being tested on.
- Write one worked example in Web Performance Optimization and time yourself on it.
Deliverable: One timed worked example in Web Performance Optimization.
04Work DSA Coding Questions
- Spend the session on DSA Coding Questions, which Glance candidates report being tested on.
- Write one worked example in DSA Coding Questions and time yourself on it.
Deliverable: One timed worked example in DSA Coding Questions.
05Answer out loud: Data Structures and Algorithms
- Answer aloud, timed: Implement a solution for a complex data structure problem (Medium/Hard level).
- Answer aloud, timed: Optimize an algorithm to reduce time complexity in a resource-constrained environment.
Deliverable: Spoken answers to 2 reported Data Structures and Algorithms question(s), under time.
06Answer out loud: Android and Mobile Architecture
- Answer aloud, timed: Explain your approach to Clean Architecture in Android development.
- Answer aloud, timed: How do you handle network latency and data synchronization using caching strategies?
Deliverable: Spoken answers to 2 reported Android and Mobile Architecture question(s), under time.
07Answer out loud: System Design
- Answer aloud, timed: Design a system for content delivery that accounts for low-end device limitations.
- Answer aloud, timed: How would you architect a backend service to handle high-frequency content updates?
Deliverable: Spoken answers to 2 reported System Design 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 network latency and data synchronization using caching strategies?
How do you handle network latency and data synchronization using caching strategies?
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?
Walk me through a challenging technical project you led.
Walk me through a challenging technical project you led.
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 disagreements with stakeholders regarding technical debt?
How do you handle disagreements with stakeholders regarding 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?
Describe a time you had to pivot your approach due to shifting project requirements.
Describe a time you had to pivot your approach due to shifting project requirements.
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 network latency and data synchronization using caching strategies?
- 02
Walk me through a challenging technical project you led.
- 03
How do you handle disagreements with stakeholders regarding technical debt?
- 04
Describe a time you had to pivot your approach due to shifting project requirements.
How long should I spend preparing?
Dedicate at least 3–4 weeks to focused practice. Prioritize coding challenges and reviewing your own past project architectures in detail.
Glance Software Engineer candidate reports ↗What is the most common reason for rejection?
Candidates often struggle when they cannot justify their architectural decisions or when they fail to demonstrate a deep enough understanding of performance optimization under hardware constraints.
Glance Software Engineer candidate reports ↗How is the culture at Glance?
Glance is a fast-paced environment that rewards ownership and technical rigor. You will be expected to take charge of your tasks and provide solutions that are both creative and efficient.
Glance Software Engineer candidate reports ↗What is the typical turnaround time?
While the process can be quick, there have been reports of delays. Always follow up professionally after each round if you haven't heard back within the promised timeframe.
Glance Software Engineer candidate reports ↗What topics does Glance test in interviews?
Glance interviews most often cover Recommendation Systems, End-to-end product design, System Design, Classical Machine Learning, and Web Performance Optimization. The exact emphasis depends on the specific role you apply for.
Glance 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.
- 01Glance 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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