Glean (CA) Software Engineer Interview Guide

The 60-second brief

As a Software Engineer at Glean (CA), you stand at the center of modern enterprise knowledge search and AI platform engineering. You are responsible for architecting, building, and scaling systems that index, search, and synthesize vast amounts of heterogeneous data across disparate enterprise platforms. The engineering problems you will tackle involve low-latency retrieval, real-time permissions enforcement, complex graph traversals, and deep integration of modern language models into core search workflows. Your impact directly shapes how teams across global organizations discover critical information and automate everyday workflows. At Glean (CA), software engineering is not merely about writing clean algorithms—it requires designing robust backend architecture, optimizing high-throughput data pipelines, and building intuitive developer platform tools and APIs.

This guide is scoped to a Software Engineer candidate at Glean (CA).

Focus areas

• Data Structures (DSA)
• Algorithms
• Coding Interview Problem Solving

The Glean (CA) interview process

Glean (CA) candidates report 5 rounds over 4-6 weeks. The stages below are what candidates describe, not a published process.

Recruiter Screen

Initial screening call with a recruiter to assess fit for the role.

What to demonstrate

• Initial screening call with a recruiter to assess fit for the role
• Depth in Data Structures (DSA)

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 Phone Screens

One or two technical phone interviews focusing on algorithms and past projects.

What to demonstrate

• One or two technical phone interviews focusing on algorithms and past projects
• Depth in Data Structures (DSA)

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.

Onsite Loop

A series of intense rounds including coding, system design, and behavioral interviews.

What to demonstrate

• A series of intense rounds including coding, system design, and behavioral interviews
• Depth in Data Structures (DSA)

How to prepare

• Answer aloud and timed: Implement a Byte Pair Encoding (BPE) tokenizer by coding the train, encode, and decode routines with pair-frequency counts and threshold logic.
• Answer aloud and timed: Design a real-time prefix suggestion function getTopSuggestions(query, userDept, k) that returns top-scored suggestions matching department criteria.

Practical Coding Assignment

A 2-hour coding assignment to demonstrate coding skills under time pressure.

What to demonstrate

• A 2-hour coding assignment to demonstrate coding skills under time pressure
• Depth in Data Structures (DSA)

How to prepare

• Answer aloud and timed: Write a fully working implementation of the game logic and state validation for Connect 4.
• Answer aloud and timed: Implement an API processing pipeline that handles JSON parsing, validation, and schema transformation.

Final Decision

Review of all interview stages to make a final hiring decision.

What to demonstrate

• Review of all interview stages to make a final hiring decision
• Depth in Data Structures (DSA)

How to prepare

• Answer aloud and timed: Design a scalable enterprise search connector capable of indexing millions of documents with fine-grained access control.
• Answer aloud and timed: Design a low-level API platform service to handle high-frequency client query requests and caching layers.

Where candidates lose points

Structure your practical code clearly

In coding assessments, avoid throwing all logic into a single monolith function. Create clean helper functions, name variables descriptively, and structure code modularly.

Manage your time tightly during the 2-hour assignment

Read the entire assignment prompt carefully before writing code. Identify core requirements first, get a baseline implementation working, and then address secondary constraints or edge cases.

Practice standard string and graph algorithms

Revisit algorithms involving depth-first search, graph traversal, priority queues, and complex string manipulations (such as tokenizers or prefix trees).

Communicate your design trade-offs explicitly

During system design and live coding sessions, explain why you choose a specific data structure or architectural component over alternatives.

Questions & practice

Find the kth largest element across two sorted arrays using efficient search strategies.

Medium · Data Structures & Algorithmic Optimizati

Approach

1. Restate the input: its shape, its size, and what is guaranteed about it.
2. Name the brute-force solution and its complexity before improving on it.
3. Choose the data structure from the access pattern, not from familiarity.
4. 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 a Graph DFS solution to traverse hierarchical permissions or dependency trees.

Medium · Data Structures & Algorithmic Optimizati

Approach

1. Restate the input: its shape, its size, and what is guaranteed about it.
2. Name the brute-force solution and its complexity before improving on it.
3. Choose the data structure from the access pattern, not from familiarity.
4. 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 matrix transformation logic similar to in-place image rotation.

Medium · Data Structures & Algorithmic Optimizati

Approach

1. Restate the input: its shape, its size, and what is guaranteed about it.
2. Name the brute-force solution and its complexity before improving on it.
3. Choose the data structure from the access pattern, not from familiarity.
4. 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 multiple sorted streams or arrays, merge them efficiently using a min-heap / priority queue.

Medium · Data Structures & Algorithmic Optimizati

Approach

1. Restate the input: its shape, its size, and what is guaranteed about it.
2. Name the brute-force solution and its complexity before improving on it.
3. Choose the data structure from the access pattern, not from familiarity.
4. 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 a Byte Pair Encoding (BPE) tokenizer by coding the train, encode, and decode routines with pai

Medium · Practical Systems & Hands-on Coding

Implement a Byte Pair Encoding (BPE) tokenizer by coding the train, encode, and decode routines with pair-frequency counts and threshold logic.

Approach

1. Restate the input: its shape, its size, and what is guaranteed about it.
2. Name the brute-force solution and its complexity before improving on it.
3. Choose the data structure from the access pattern, not from familiarity.
4. State the target complexity and say which constraint rules the naive version out.

Follow-up

• How does this change if the input no longer fits in memory?
• What is the worst case, and how likely is it on real data?

Write a fully working implementation of the game logic and state validation for Connect 4.

Medium · Practical Systems & Hands-on Coding

Approach

1. Restate the input: its shape, its size, and what is guaranteed about it.
2. Name the brute-force solution and its complexity before improving on it.
3. Choose the data structure from the access pattern, not from familiarity.
4. 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?

Model SQL and database schemas to manage metadata, user activity logs, and real-time permission mapping.

Medium · System Design & Low-Level Architecture

Approach

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

Follow-up

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

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

1. 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.
2. 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.
3. 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.
4. 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?

Design a real-time prefix suggestion function getTopSuggestions(query, userDept, k) that returns top-scored

Medium · Practical Systems & Hands-on Coding

Design a real-time prefix suggestion function getTopSuggestions(query, userDept, k) that returns top-scored suggestions matching department criteria.

Approach

1. Fix the scope first: who calls this, how often, and what they do when it fails.
2. Name the read and write paths separately; they rarely have the same bottleneck.
3. Choose a partition key and say what query it makes expensive.
4. State the consistency you need, and where you are willing to be stale.

Follow-up

• What breaks first when traffic grows ten times?
• How does this behave when that dependency is down for an hour?

Implement an API processing pipeline that handles JSON parsing, validation, and schema transformation.

Medium · Practical Systems & Hands-on Coding

Approach

1. Say who the caller is and what they do when the call fails halfway.
2. Define the identity of a request so a retry cannot double-apply it.
3. Separate accepted, pending, failed and confirmed; they are different facts.
4. 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 scalable enterprise search connector capable of indexing millions of documents with fine-grained acce

Medium · System Design & Low-Level Architecture

Design a scalable enterprise search connector capable of indexing millions of documents with fine-grained access control.

Approach

1. Fix the scope first: who calls this, how often, and what they do when it fails.
2. Name the read and write paths separately; they rarely have the same bottleneck.
3. Choose a partition key and say what query it makes expensive.
4. 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 low-level API platform service to handle high-frequency client query requests and caching layers.

Medium · System Design & Low-Level Architecture

Approach

1. Fix the scope first: who calls this, how often, and what they do when it fails.
2. Name the read and write paths separately; they rarely have the same bottleneck.
3. Choose a partition key and say what query it makes expensive.
4. 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 an autocompletion and query parsing microservice optimized for sub-100ms response times.

Medium · System Design & Low-Level Architecture

Approach

1. Fix the scope first: who calls this, how often, and what they do when it fails.
2. Name the read and write paths separately; they rarely have the same bottleneck.
3. Choose a partition key and say what query it makes expensive.
4. 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 through the most complex system architecture you have personally designed and deployed to production.

Medium · Behavioral & Background Deep-Dive

Approach

1. Fix the scope first: who calls this, how often, and what they do when it fails.
2. Name the read and write paths separately; they rarely have the same bottleneck.
3. Choose a partition key and say what query it makes expensive.
4. 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 log partition stops advancing while the others drain

Easy · poison message · head-of-line blocking · consumer lag

Search results for a subset of tenants are hours stale; the rest are current. The projection consumer reports lag of zero on 15 of 16 partitions and 400,000 on one. Its error rate is flat and its CPU is idle. outbox_event has no pending rows older than a second, so the relay has published everything it holds. Identify the mechanism, give the ordered checks, and state what you do in the first ten minutes versus what you change permanently.

Approach

1. Read the lag distribution first. A slow consumer lags everywhere; zero on fifteen partitions and 400,000 on one is not throughput. Idle CPU on the stuck partition means the consumer is not advancing its offset at all, which points at one message it cannot get past rather than at a rate problem.
2. Exonerate the producer before touching the consumer. No pending outbox rows older than a second means the relay published, so the event exists in the log. This separates never sent from sent and never applied, which are different code paths and usually different owners.
3. Read the message at the stuck offset and the handler's log lines for its event_id. A flat error rate with no progress has two explanations and you must distinguish them: the handler is throwing and the retry loop is swallowing it, or the handler is blocking on something and never returning. Idle CPU with no error lines favours the second.
4. Mitigate before diagnosing further. Move the offending event to a dead-letter store and commit the offset past it. Adding consumers does nothing here, because a partition is consumed by exactly one member of the group, and the blast radius is every aggregate hashed to that partition, not only the aggregate that produced the bad event.

Follow-up

• The dead-lettered event carried aggregate_version 7 and the projection had applied 6. What must the replay do differently if 8 and 9 landed in the meantime?
• How do you show staleness to the user while the partition is behind, given the API already returns the projection's watermark?

Suggested practice checklist

Built from the rounds and topics Glean (CA) candidates report.

Day 1 — Map the Glean (CA) loop

• Write out the reported sequence: Recruiter Screen, Technical Phone Screens, Onsite Loop, Practical Coding Assignment, Final Decision.
• 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 5 reported rounds, with the weakest marked.

Day 2 — Work Data Structures (DSA)

• Spend the session on Data Structures (DSA), which Glean (CA) candidates report being tested on.
• Write one worked example in Data Structures (DSA) and time yourself on it.

Deliverable: One timed worked example in Data Structures (DSA).

Day 3 — Work Algorithms

• Spend the session on Algorithms, which Glean (CA) candidates report being tested on.
• Write one worked example in Algorithms and time yourself on it.

Deliverable: One timed worked example in Algorithms.

Day 4 — Work Coding Interview Problem Solving

• Spend the session on Coding Interview Problem Solving, which Glean (CA) candidates report being tested on.
• Write one worked example in Coding Interview Problem Solving and time yourself on it.

Deliverable: One timed worked example in Coding Interview Problem Solving.

Day 5 — Answer out loud: Data Structures & Algorithmic Optimization

• Answer aloud, timed: Find the kth largest element across two sorted arrays using efficient search strategies.
• Answer aloud, timed: Implement a Graph DFS solution to traverse hierarchical permissions or dependency trees.

Deliverable: Spoken answers to 2 reported Data Structures & Algorithmic Optimization question(s), under time.

Day 6 — Answer out loud: Practical Systems & Hands-on Coding

• Answer aloud, timed: Implement a Byte Pair Encoding (BPE) tokenizer by coding the train, encode, and decode routines with pair-frequency counts and threshold logic.
• Answer aloud, timed: Design a real-time prefix suggestion function getTopSuggestions(query, userDept, k) that returns top-scored suggestions matching department criteria.

Deliverable: Spoken answers to 2 reported Practical Systems & Hands-on Coding question(s), under time.

Day 7 — Answer out loud: System Design & Low-Level Architecture

• Answer aloud, timed: Design a scalable enterprise search connector capable of indexing millions of documents with fine-grained access control.
• Answer aloud, timed: Design a low-level API platform service to handle high-frequency client query requests and caching layers.

Deliverable: Spoken answers to 2 reported System Design & Low-Level Architecture question(s), under time.

Behavioural preparation

Behavioural rounds judge the decision you made and what it cost.

Describe a situation where you had to make a tough technical compromise to meet a tight product deadline.

Medium · Behavioral & Background Deep-Dive

Approach

1. Pick a story where you made the decision, not one where you watched it.
2. State the situation in two sentences and spend the rest on the reasoning.
3. Give the blast radius: what could have broken, and what you measured.
4. 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 ambiguous requirements when building a new backend feature or platform API?

Medium · Behavioral & Background Deep-Dive

Approach

1. Pick a story where you made the decision, not one where you watched it.
2. State the situation in two sentences and spend the rest on the reasoning.
3. Give the blast radius: what could have broken, and what you measured.
4. 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 resolved a major production outage or deep technical regression under high pressure.

Medium · Behavioral & Background Deep-Dive

Approach

1. Pick a story where you made the decision, not one where you watched it.
2. State the situation in two sentences and spend the rest on the reasoning.
3. Give the blast radius: what could have broken, and what you measured.
4. 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?