Verily Software Engineer Interview Guide

The 60-second brief

As a Software Engineer at Verily (an Alphabet subsidiary), you sit at the intersection of advanced technology and healthcare innovation. Launched from Google X in 2015, Verily focuses on bringing the promise of precision health to everyone by closing the gap between clinical research and care delivery. In this role, you will build software systems that generate, organize, and activate complex data from clinical trials, wearable health-monitoring sensors, and real-world medical evidence. Engineering at Verily involves working on high-impact products and platforms such as the Precision Health Platform, Viewpoint enterprise applications, and cloud-native infrastructure like Cortex Infra.

This guide is scoped to a Software Engineer candidate at Verily.

Focus areas

• Data Structures & Algorithms (DSA)
• System Design
• Algorithmic Coding Challenges

The Verily interview process

Verily candidates report 4 rounds over 3-5 weeks. The stages below are what candidates describe, not a published process.

Screening Call

Initial call to evaluate candidate's background and fit for the role.

What to demonstrate

• Initial call to evaluate candidate's background and fit for the role
• Depth in Data Structures & Algorithms (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 Assessments

Multiple rounds of interviews focusing on technical skills.

What to demonstrate

• Multiple rounds of interviews focusing on technical skills
• Depth in Data Structures & Algorithms (DSA)

How to prepare

• Answer aloud and timed: Solve an array manipulation problem requiring optimized sliding window or two-pointer traversal techniques.
• Answer aloud and timed: Develop a pathfinding solution across a complex tree or grid using Depth-First Search (DFS) or Breadth-First Search (BFS).

System Design Discussions

Interviews that assess the candidate's ability to design systems.

What to demonstrate

• Interviews that assess the candidate's ability to design systems
• Depth in Data Structures & Algorithms (DSA)

How to prepare

• Answer aloud and timed: Design and implement an in-memory data structure that efficiently balances lookup and insertion operations using a combination of a hash map and a priority queue.
• Answer aloud and timed: Design a real-time data ingestion pipeline capable of capturing, streaming, and processing continuous sensor telemetry from wearable devices.

Behavioral Interviews

Interviews to evaluate interpersonal skills and alignment with company values.

What to demonstrate

• Interviews to evaluate interpersonal skills and alignment with company values
• Depth in Data Structures & Algorithms (DSA)

How to prepare

• Prepare three examples from your own work, each with a decision you made and an outcome you can quantify.
• Re-read the description of the behavioral interviews above and write down what you would ask to confirm before it.

Where candidates lose points

Always clarify requirements early

Interviewers at Verily intentionally present open-ended coding and system design prompts. Before jumping into code, ask clarifying questions to define inputs, constraints, volume metrics, and unexpected edge cases.

Verbalize your technical logic

Do not code in silence. Continuously communicate your thought process, evaluate trade-offs out loud, and explain why you chose a specific data structure or algorithmic approach over another.

Practice Google-style live coding

Familiarize yourself with writing clean code in simple text editors or shared documents without reliance on heavy IDE auto-completion or line-by-line debuggers.

Questions & practice

Implement a basic expression evaluation system or custom calculator supporting variable operator precedence.

Medium · Data Structures & Algorithms

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 a network of entities, construct an algorithm to trace and return direct and indirect relationships with

Medium · Data Structures & Algorithms

Given a network of entities, construct an algorithm to trace and return direct and indirect relationships within a graph structure.

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?

Solve an array manipulation problem requiring optimized sliding window or two-pointer traversal techniques.

Medium · Data Structures & Algorithms

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?

Develop a pathfinding solution across a complex tree or grid using Depth-First Search (DFS) or Breadth-First S

Medium · Data Structures & Algorithms

Develop a pathfinding solution across a complex tree or grid using Depth-First Search (DFS) or Breadth-First Search (BFS).

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 an in-memory caching module that handles thread safety, cache eviction strategies, and concurrent ac

Medium · Object-Oriented & Application Design

Implement an in-memory caching module that handles thread safety, cache eviction strategies, and concurrent access.

Approach

1. Say what the runtime actually does before reasoning about the code.
2. Name what is shared across threads and what owns each piece of state.
3. Identify the window where an invariant is briefly untrue.
4. 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?

Write a low-level algorithm managing pointer manipulations or bitwise array operations for resource-constraine

Medium · Object-Oriented & Application Design

Write a low-level algorithm managing pointer manipulations or bitwise array operations for resource-constrained device communication.

Approach

1. Say what the runtime actually does before reasoning about the code.
2. Name what is shared across threads and what owns each piece of state.
3. Identify the window where an invariant is briefly untrue.
4. 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?

Explain why the owner filter ignores the listing index

Medium · composite index · query plans · sort elimination

The only index on resource is (tenant_id, status, updated_at DESC, resource_id DESC). A new endpoint returns one user's resources across all statuses, newest created first: WHERE tenant_id = $1 AND owner_user_id = $2 ORDER BY created_at DESC LIMIT 20. On a tenant with 2M rows it takes 900 ms and EXPLAIN shows a sort above a large scan. Explain precisely why the existing index cannot serve it, give the index that can, and state which of these the new index still will not help: owner_user_id alone across tenants; the same query ordered by updated_at. PostgreSQL 16.

Approach

1. Separate the two jobs an index does. For filtering, a composite btree is seekable only on a left prefix, so with no predicate on status the scan can at best range over tenant_id and test owner_user_id per row; PostgreSQL 16 has no btree skip scan to jump the unconstrained column.
2. For ordering, the index is sorted by (status, updated_at) within a tenant and not by created_at, so the LIMIT cannot stop early: every matching row is read and then sorted. That is the 'Sort Method: top-N heapsort' line, and it is why the plan reads 2M rows to answer with 20.
3. Derive the replacement from the access path — equality, equality, then the ordering column: CREATE INDEX CONCURRENTLY ON resource (tenant_id, owner_user_id, created_at DESC). The scan seeks to the (tenant, owner) range and walks 20 entries in order, so the Sort node disappears along with the row-read.
4. Treat INCLUDE (title, status) as conditional, not free. An index-only scan still visits the heap for any row whose page is not marked all-visible, so on a table taking 1.2k writes/second the win depends on autovacuum keeping the visibility map current, and the wider index costs more on every insert.

Follow-up

• 90% of rows are status='active'. Would a partial index WHERE status = 'active' change your answer, and for which of the three queries?
• A dashboard runs this for 40 owners in one page load. What changes about the design?

Denormalise tenant onto revisions and backfill it live

Hard · denormalisation · online migration · backfill

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

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

Design and implement an in-memory data structure that efficiently balances lookup and insertion operations usi

Medium · Data Structures & Algorithms

Design and implement an in-memory data structure that efficiently balances lookup and insertion operations using a combination of a hash map and a priority queue.

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 real-time data ingestion pipeline capable of capturing, streaming, and processing continuous sensor t

Medium · System Design & Cloud Architecture

Design a real-time data ingestion pipeline capable of capturing, streaming, and processing continuous sensor telemetry from wearable devices.

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?

Architect a peer-to-peer (P2P) mobile payment and transaction engine, accounting for backend redundancy, failo

Medium · System Design & Cloud Architecture

Architect a peer-to-peer (P2P) mobile payment and transaction engine, accounting for backend redundancy, failover, and high availability.

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 enterprise content configuration and localization platform for multi-region clinical trials.

Medium · System Design & Cloud 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?

Structure a cloud-native continuous integration and deployment (CI/CD) pipeline utilizing container orchestrat

Medium · System Design & Cloud Architecture

Structure a cloud-native continuous integration and deployment (CI/CD) pipeline utilizing container orchestrators like Kubernetes.

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 an end-to-end audit logging system that tracks high-volume user access to sensitive clinical records wh

Medium · System Design & Cloud Architecture

Design an end-to-end audit logging system that tracks high-volume user access to sensitive clinical records while maintaining strict compliance requirements.

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 extensible, object-oriented system for managing clinical trial protocol schedules and participant ev

Medium · Object-Oriented & Application Design

Design an extensible, object-oriented system for managing clinical trial protocol schedules and participant event triggers.

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?

Refactor a complex monolithic function into modular, testable components using appropriate design patterns.

Medium · Object-Oriented & Application Design

Approach

1. Clarify what is being asked and what a complete answer contains.
2. State your assumptions explicitly before working the problem.
3. Say what you would check first and why it is the highest-information step.
4. Work from the requirement backwards to the design.

Follow-up

• What assumption would you test first?
• How would you know your answer was wrong?

One customer endpoint stalls deliveries to every other destination

Medium · bulkhead · timeouts · saturation

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

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

Suggested practice checklist

Built from the rounds and topics Verily candidates report.

Day 1 — Map the Verily loop

• Write out the reported sequence: Screening Call, Technical Assessments, System Design Discussions, Behavioral Interviews.
• 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.

Day 2 — Work Data Structures & Algorithms (DSA)

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

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

Day 3 — Work System Design

• Spend the session on System Design, which Verily candidates report being tested on.
• Write one worked example in System Design and time yourself on it.

Deliverable: One timed worked example in System Design.

Day 4 — Work Algorithmic Coding Challenges

• Spend the session on Algorithmic Coding Challenges, which Verily candidates report being tested on.
• Write one worked example in Algorithmic Coding Challenges and time yourself on it.

Deliverable: One timed worked example in Algorithmic Coding Challenges.

Day 5 — Answer out loud: Data Structures & Algorithms

• Answer aloud, timed: Implement a basic expression evaluation system or custom calculator supporting variable operator precedence.
• Answer aloud, timed: Given a network of entities, construct an algorithm to trace and return direct and indirect relationships within a graph structure.

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

Day 6 — Answer out loud: System Design & Cloud Architecture

• Answer aloud, timed: Design a real-time data ingestion pipeline capable of capturing, streaming, and processing continuous sensor telemetry from wearable devices.
• Answer aloud, timed: Architect a peer-to-peer (P2P) mobile payment and transaction engine, accounting for backend redundancy, failover, and high availability.

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

Day 7 — Answer out loud: Object-Oriented & Application Design

• Answer aloud, timed: Design an extensible, object-oriented system for managing clinical trial protocol schedules and participant event triggers.
• Answer aloud, timed: Implement an in-memory caching module that handles thread safety, cache eviction strategies, and concurrent access.

Deliverable: Spoken answers to 2 reported Object-Oriented & Application Design question(s), under time.

Behavioural preparation

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

Describe a time when you had to make a critical architecture decision with incomplete specifications or ambigu

Medium · Behavioral & Leadership (Googley & Cultu

Describe a time when you had to make a critical architecture decision with incomplete specifications or ambiguous requirements.

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?

Walk through a past technical initiative where you had to manage risk, mitigate scope creep, and align enginee

Medium · Behavioral & Leadership (Googley & Cultu

Walk through a past technical initiative where you had to manage risk, mitigate scope creep, and align engineering stakeholders.

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 approach providing constructiveness in code reviews while driving high engineering quality standard

Medium · Behavioral & Leadership (Googley & Cultu

How do you approach providing constructiveness in code reviews while driving high engineering quality standards?

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?

Share an experience where you resolved a major technical disagreement with a cross-functional partner, such as

Medium · Behavioral & Leadership (Googley & Cultu

Share an experience where you resolved a major technical disagreement with a cross-functional partner, such as a product manager or data scientist.

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?