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. Whether you are building distributed backend services in Go or Java, architecting cloud infrastructure on Google Cloud Platform (GCP), or developing full-stack clinical workflow applications with and, your code directly influences how patient data is analyzed and transformed into life-saving medical insights. TypeScript React The technical challenges at Verily demand a combination of algorithmic rigor, system architecture expertise, and an appreciation for operating within regulated medical environments (including compliance standards like HIPAA and GDPR). You will solve complex problems around high-throughput data ingestion, digital biomarker processing, and cloud-native microservices scale while collaborating closely with software leaders, data scientists, and healthcare domain experts.
Screening Call
reportedInitial 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
reportedMultiple 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
reportedInterviews 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
reportedInterviews 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.
1 candidate reports. Individual accounts describe a particular role and hiring cycle.
Verily Software Engineer interview: coding and an HR step
I applied online and then followed recruiter-driven scheduling. They reviewed my resume, explained the process, and moved me into a technical round before an HR step. The technical part matched what I expected for a software engineering interview there: coding-focused questions in the easy-to-medium range, with nothing that felt far off-script. It felt easier than processes with heavy design or d…
Read full experiencePracHub editorial advice for the preparation topics above.
Going into the loop without having done this.
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.
Going into the loop without having done this.
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.
Going into the loop without having done this.
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.
Going into the loop without having done this.
When solving algorithmic coding problems, explicitly analyze and state both the Time Complexity and Space Complexity (Big-O notation) of your solution before and after writing your implementation.
Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.
Implement a basic expression evaluation system or custom calculator supporting variable operator precedence.
Implement a basic expression evaluation system or custom calculator supporting variable operator precedence.
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 network of entities, construct an algorithm to trace and return direct and indirect relationships with
Given a network of entities, construct an algorithm to trace and return direct and indirect relationships within a graph structure.
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 an array manipulation problem requiring optimized sliding window or two-pointer traversal techniques.
Solve an array manipulation problem requiring optimized sliding window or two-pointer traversal techniques.
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?
Develop a pathfinding solution across a complex tree or grid using Depth-First Search (DFS) or Breadth-First S
Develop a pathfinding solution across a complex tree or grid using Depth-First Search (DFS) or Breadth-First Search (BFS).
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 in-memory caching module that handles thread safety, cache eviction strategies, and concurrent ac
Implement an in-memory caching module that handles thread safety, cache eviction strategies, and concurrent access.
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?
Write a low-level algorithm managing pointer manipulations or bitwise array operations for resource-constraine
Write a low-level algorithm managing pointer manipulations or bitwise array operations for resource-constrained device communication.
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?
Explain why the owner filter ignores the listing index
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
- 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.
- 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.
- 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.
- 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
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?
Design and implement an in-memory data structure that efficiently balances lookup and insertion operations usi
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
- 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 real-time data ingestion pipeline capable of capturing, streaming, and processing continuous sensor t
Design a real-time data ingestion pipeline capable of capturing, streaming, and processing continuous sensor telemetry from wearable 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?
Architect a peer-to-peer (P2P) mobile payment and transaction engine, accounting for backend redundancy, failo
Architect a peer-to-peer (P2P) mobile payment and transaction engine, accounting for backend redundancy, failover, and high availability.
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 enterprise content configuration and localization platform for multi-region clinical trials.
Design a enterprise content configuration and localization platform for multi-region clinical trials.
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?
Structure a cloud-native continuous integration and deployment (CI/CD) pipeline utilizing container orchestrat
Structure a cloud-native continuous integration and deployment (CI/CD) pipeline utilizing container orchestrators like Kubernetes.
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 an end-to-end audit logging system that tracks high-volume user access to sensitive clinical records wh
Design an end-to-end audit logging system that tracks high-volume user access to sensitive clinical records while maintaining strict compliance requirements.
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 an extensible, object-oriented system for managing clinical trial protocol schedules and participant ev
Design an extensible, object-oriented system for managing clinical trial protocol schedules and participant event triggers.
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?
Refactor a complex monolithic function into modular, testable components using appropriate design patterns.
Refactor a complex monolithic function into modular, testable components using appropriate design patterns.
Approach
- Clarify what is being asked and what a complete answer contains.
- State your assumptions explicitly before working the problem.
- Say what you would check first and why it is the highest-information step.
- Work from the requirement backwards to the design.
Follow-up
- What assumption would you test first?
- How would you know your answer was wrong?
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 Verily candidates report.
Prepare, practise & reflect
One practical outcome each day. Spend longer where you need it.
0 / 7 done01Map 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.
02Work 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).
03Work 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.
04Work 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.
05Answer 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.
06Answer 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.
07Answer 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.
Expand any day for tasks and deliverables. Your progress is saved on this device.
Behavioural rounds judge the decision you made and what it cost.
Describe a time when you had to make a critical architecture decision with incomplete specifications or ambigu
Describe a time when you had to make a critical architecture decision with incomplete specifications or ambiguous 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?
Walk through a past technical initiative where you had to manage risk, mitigate scope creep, and align enginee
Walk through a past technical initiative where you had to manage risk, mitigate scope creep, and align engineering stakeholders.
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 providing constructiveness in code reviews while driving high engineering quality standard
How do you approach providing constructiveness in code reviews while driving high engineering quality standards?
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?
Share an experience where you resolved a major technical disagreement with a cross-functional partner, such as
Share an experience where you resolved a major technical disagreement with a cross-functional partner, such as a product manager or data scientist.
Approach
- Pick a story where you made the decision, not one where you watched it.
- State the situation in two sentences and spend the rest on the reasoning.
- Give the blast radius: what could have broken, and what you measured.
- Name the disagreement and how you resolved it with evidence.
Follow-up
- What would you do differently if you ran that again?
- How did you know your change caused the improvement?
- 01
Describe a time when you had to make a critical architecture decision with incomplete specifications or ambiguous requirements.
- 02
Walk through a past technical initiative where you had to manage risk, mitigate scope creep, and align engineering stakeholders.
- 03
How do you approach providing constructiveness in code reviews while driving high engineering quality standards?
- 04
Share an experience where you resolved a major technical disagreement with a cross-functional partner, such as a product manager or data scientist.
How difficult are the technical interviews at Verily compared to FAANG companies?
The technical bar at Verily is comparable to Google and other top tier tech companies. You should expect rigorous data structure and algorithm challenges, coupled with in-depth system design loops for mid-level and senior roles. Thorough preparation on core CS fundamentals is essential.
Verily Software Engineer candidate reports ↗What programming language should I use during the live coding assessments?
You can generally choose any mainstream language you are most comfortable with, such as Go, Java, Python, C++, or TypeScript. It is best to stick to the language in which you can write clean, syntactically accurate code quickly under time pressure.
Verily Software Engineer candidate reports ↗How does the team matching phase work at Verily?
After successfully passing the technical hiring loop, candidates enter team matching. During this phase, your profile is shared with hiring managers across different product groups (such as Precision Health Platform, Viewpoint, or Sensors Suite) to match your specific expertise and interests with open team headcounts.
Verily Software Engineer candidate reports ↗What is the typical timeline from initial screen to job offer?
The full process typically takes between four and eight weeks, depending on scheduling availability, team matching requirements, and committee reviews. Staying in frequent communication with your recruiter helps keep the pipeline moving efficiently.
Verily Software Engineer candidate reports ↗Are engineering roles at Verily open to remote work?
Verily offers a hybrid model across key engineering hubs, including Waterloo (ON), Mountain View (CA), San Bruno (CA), Boston (MA), and Raleigh (NC), alongside selected fully remote positions depending on team requirements and role level.
Verily Software Engineer candidate reports ↗How hard is the Verily interview?
Candidates most commonly rate Verily interviews as medium, based on 243 reported interviews. About 21% of candidates who interview go on to receive an offer.
Verily Software Engineer candidate reports ↗What topics does Verily test in interviews?
Verily interviews most often cover Cross-functional Collaboration, SQL, Problem Solving, Data Structures & Algorithms (DSA), and Python. The exact emphasis depends on the specific role you apply for.
Verily Software Engineer candidate reports ↗Is Verily a good place to work?
Employees rate Verily 2.9 out of 5 overall, based on aggregated workplace reviews spanning career growth, work-life balance, compensation, culture, and management.
Verily Software Engineer candidate reports ↗Where is Verily headquartered?
Verily is headquartered in Dallas, TX.
Verily Software Engineer candidate reports ↗Sources & methodology 3 sources ↗
Official role evidence, timestamped platform data and clearly labeled preparation advice.
- 01Verily Software Engineer candidate reports ↗
Company-reported rounds, questions and FAQ.
candidate · Accessed 2026-09-22 - 02PracHub Software Engineer practice ↗
PracHub practice material, not company-reported.
platform · Accessed 2026-09-22 - 03PracHub preparation framework ↗
PracHub preparation guidance.
platform · Accessed 2026-09-22
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