As a Software Engineer at Thorn, you will play a pivotal role in developing cutting-edge technology aimed at combating child sexual exploitation and enhancing online safety. Your contributions will directly impact the effectiveness of Thorn's tools and initiatives, which are designed to empower organizations and individuals to protect vulnerable children. The work you do will not only involve solving complex technical problems but also contribute to meaningful social change, making this role both challenging and rewarding. In this position, you will collaborate with cross-functional teams, including product managers, designers, and other engineers, to design and implement robust software solutions. You will tackle a variety of projects, from enhancing existing systems to developing new features across Thorn's platforms, which serve a diverse set of users, including law enforcement agencies and child protection organizations. This dynamic environment demands creativity, technical expertise, and a passion for using technology to drive social impact.
Application Review
reportedInitial review of your application to assess qualifications and fit for the role.
What to demonstrate
- Initial review of your application to assess qualifications and fit for the role
- Depth in Communication Skills
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 Screen
reportedA preliminary technical assessment to evaluate your technical skills and knowledge.
What to demonstrate
- A preliminary technical assessment to evaluate your technical skills and knowledge
- Depth in Communication Skills
How to prepare
- Answer aloud and timed: How do you ensure the security and privacy of data in your applications?
- Answer aloud and timed: Describe your experience with cloud technologies and how you have used them in past projects.
In-Depth Interviews
reportedSeveral interviews focusing on both behavioral and technical assessments.
What to demonstrate
- Several interviews focusing on both behavioral and technical assessments
- Depth in Communication Skills
How to prepare
- Answer aloud and timed: How would you approach designing a system to handle large-scale data processing?
- Answer aloud and timed: Discuss a system you designed from scratch. What were the key considerations?
PracHub editorial advice for the preparation topics above.
Understand Thorn's mission
Familiarize yourself with Thorn's work and how technology plays a role in child protection. This knowledge will resonate during your interviews and demonstrate your alignment with the organization's goals.
Prepare for behavioral interviews
Develop stories that highlight your teamwork, problem-solving skills, and adaptability. Use the STAR method (Situation, Task, Action, Result) to structure your responses effectively.
Practice coding problems
Although some interviews may not involve direct coding challenges, being prepared for algorithmic questions will help reinforce your technical skills and confidence.
Be ready for system design discussions
Expect to discuss real-world applications of your design choices. Think about scalability and performance in your previous projects to articulate your thought process clearly.
Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.
Write a function to reverse a linked list. Explain your thought process.
Write a function to reverse a linked list. Explain your thought process.
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?
How would you optimize a search function in a large dataset?
How would you optimize a search function in a large dataset?
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?
Merge partitioned event streams into one ordered feed with bounded lateness
The read-model service consumes 64 log partitions carrying about 4,000 events per second in total. Each partition is ordered within itself, but partitions drift by up to 30 seconds, and the activity feed must present a tenant's events in occurred_at order. Produce the merge. State its complexity, the buffer it requires in events and in bytes, what happens when one partition is idle, and what you do with an event that arrives after you have already emitted its position. Payloads average 1 KB.
Approach
- Merge with a min-heap over the 64 partition heads keyed on (occurred_at, event_id): O(log P) per event and O(n log P) overall. The tie-break on event_id is what makes the output deterministic when two partitions carry the same millisecond, which matters because the feed is paginated and a non-deterministic order reorders pages under the reader.
- Emitting the heap head is only correct once every partition has produced everything up to that timestamp, so the emit condition is a watermark: the minimum across partitions of the highest occurred_at seen, less the allowed lateness. Events are held until the watermark passes them, which is what turns individually ordered streams into a jointly ordered one.
- Size the buffer from the lateness rather than guessing: 4,000 events per second times 30 seconds is 120,000 buffered events, and at 1 KB each about 120 MB of heap. That number is the real price of the ordering guarantee and belongs in front of whoever asked for it.
- Handle the idle partition explicitly, because it fails the feed rather than corrupting it: a partition with no traffic never advances its own maximum, so the watermark freezes and output stops entirely. Either every partition emits a periodic idle marker carrying the broker's current time, or the watermark falls back to wall clock for a partition silent beyond a threshold.
Follow-up
- The lateness budget is raised to five minutes. What is the new buffer, and what besides memory changes?
- The consumer restarts. Where does it resume from, and what does the feed look like for the first 30 seconds?
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?
Stop tag and share joins from fanning out a page
resource_tag is (resource_id, tag_id) with PK (resource_id, tag_id); resource_share is (resource_id, shared_with_user_id, permission). The tagged-and-shared listing inner-joins resource to both, filters tenant_id, tag_id = ANY($2) and shared_with_user_id = $3, orders by updated_at DESC and takes 50. Pages come back with fewer than 50 distinct resources and the total in the header is far too high. Explain the row multiplication, rewrite both the page query and the count query so each is correct, and name the index each one needs. PostgreSQL 16.
Approach
- Do the arithmetic against the predicates that are actually there. An inner join emits one row per matching child row, and both joins are filtered: tag_id = ANY($2) admits only the requested tags, shared_with_user_id = $3 admits one user's share rows. So a resource holding three of the requested tags and shared with $3 once yields three rows, not one — the multiplier is its count of matching tags times its share rows for that single user, and that second factor is 1 unless the table admits duplicate (resource_id, shared_with_user_id) pairs. LIMIT 50 then limits rows rather than resources, and COUNT(*) counts pairs — the header is the product, not the population.
- Reject DISTINCT as the fix. It deduplicates after the product has been built, so the planner must materialise and sort the fanned-out set before the LIMIT can apply, and it leaves any SUM or AVG in the same select list wrong.
- Rewrite both filters as semi-joins, keeping resource as the only row source: AND EXISTS (SELECT 1 FROM resource_tag rt WHERE rt.resource_id = r.resource_id AND rt.tag_id = ANY($2)) and the same shape against resource_share. A semi-join stops at the first match per resource and preserves the driving index order, so ORDER BY updated_at DESC, resource_id DESC LIMIT 50 still stops after 50 rows.
- Count with the same predicates and no join at all: SELECT count(*) FROM resource r WHERE r.tenant_id = $1 AND r.status = 'active' AND EXISTS (...) AND EXISTS (...). Nothing multiplies a resource, so the number is the population.
Follow-up
- The filter changes from 'any of these tags' to 'all of these tags'. Rewrite it and state what it costs relative to the ANY form.
- A resource can be shared with the same user twice under different permissions. Does your count change, and should it?
What programming languages are you most proficient in, and how have you applied them in previous projects?
What programming languages are you most proficient in, and how have you applied them in previous projects?
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?
Explain a challenging technical problem you encountered and how you resolved it.
Explain a challenging technical problem you encountered and how you resolved it.
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?
How do you ensure the security and privacy of data in your applications?
How do you ensure the security and privacy of data in your applications?
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?
How would you approach designing a system to handle large-scale data processing?
How would you approach designing a system to handle large-scale data processing?
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 a system you designed from scratch. What were the key considerations?
Discuss a system you designed from scratch. What were the key considerations?
Approach
- Fix the scope first: who calls this, how often, and what they do when it fails.
- Name the read and write paths separately; they rarely have the same bottleneck.
- Choose a partition key and say what query it makes expensive.
- State the consistency you need, and where you are willing to be stale.
Follow-up
- What breaks first when traffic grows ten times?
- How does this behave when that dependency is down for an hour?
How would you design an API for a new feature in our platform?
How would you design an API for a new feature in our platform?
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?
You have been tasked with improving an existing feature that is underperforming. What steps would you take?
You have been tasked with improving an existing feature that is underperforming. What steps would you take?
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?
How would you approach a situation where your team is falling behind on deadlines?
How would you approach a situation where your team is falling behind on deadlines?
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?
Edge instances grow 400 MB per hour until the nightly restart
Edge API instances start at 700 MB resident and grow about 400 MB/hour; a nightly rolling restart has hidden it for weeks. Growth continues unchanged when request rate halves overnight, p99 degrades in the last hours before an instance is recycled, and heap used immediately after a forced full GC rises monotonically. The service holds no product state. Name the discriminating measurement that separates the plausible causes, give the most likely cause, and give the fix and how you would verify it.
Approach
- Separate resident memory from live heap first, because they fail differently. Resident size can grow from fragmentation, native buffers or thread stacks while the heap is flat; heap used after a full GC rising monotonically is the measurement that says objects are reachable and not being released. You already have it, so this is retention, not fragmentation, and that closes off half the candidate list.
- Use the rate's independence from traffic as the discriminator. Growth that continues at half the request rate rules out per-request objects that are merely slow to collect and points at a structure that grows with distinct values observed rather than with call volume. Write the candidates that have that property: a metrics registry keyed on a high-cardinality label, an unevicted cache, an interner, a per-key lock map.
- Take two heap snapshots an hour apart and diff by retained size, reading the dominator tree, not by allocation count or instance count. Expect one root holding a map with millions of entries, then follow the reference chain to the code that inserts and never removes. Allocation profilers point at churn, which is the wrong signal here.
- The candidate that fits this service is an observability label carrying an identifier, such as a request path recorded before templating so that /v1/resources/48213 becomes its own metric series. That grows with distinct ids seen, is independent of rate, and explains the late p99 degradation, since GC cost rises with the size of the live set.
Follow-up
- Post-GC heap is now flat but resident size still creeps. What are you looking at, and does it matter?
- How would you have detected this before an OOM, given the nightly restart masked the trend?
Built from the rounds and topics Thorn candidates report.
Prepare, practise & reflect
One practical outcome each day. Spend longer where you need it.
0 / 7 done01Map the Thorn loop
- Write out the reported sequence: Application Review, Technical Screen, In-Depth 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 3 reported rounds, with the weakest marked.
02Work Communication Skills
- Spend the session on Communication Skills, which Thorn candidates report being tested on.
- Write one worked example in Communication Skills and time yourself on it.
Deliverable: One timed worked example in Communication Skills.
03Work System Design
- Spend the session on System Design, which Thorn 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 Software Architecture & Technical Decision-Making
- Spend the session on Software Architecture & Technical Decision-Making, which Thorn candidates report being tested on.
- Write one worked example in Software Architecture & Technical Decision-Making and time yourself on it.
Deliverable: One timed worked example in Software Architecture & Technical Decision-Making.
05Answer out loud: Technical / Domain Questions
- Answer aloud, timed: What programming languages are you most proficient in, and how have you applied them in previous projects?
- Answer aloud, timed: Explain a challenging technical problem you encountered and how you resolved it.
Deliverable: Spoken answers to 2 reported Technical / Domain Questions question(s), under time.
06Answer out loud: System Design / Architecture
- Answer aloud, timed: How would you approach designing a system to handle large-scale data processing?
- Answer aloud, timed: Discuss a system you designed from scratch. What were the key considerations?
Deliverable: Spoken answers to 2 reported System Design / Architecture question(s), under time.
07Answer out loud: Behavioral / Leadership
- Answer aloud, timed: Describe a time when you had to advocate for a technical decision. How did you handle it?
- Answer aloud, timed: How do you prioritize your tasks when working on multiple projects?
Deliverable: Spoken answers to 2 reported Behavioral / Leadership 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 your experience with cloud technologies and how you have used them in past projects.
Describe your experience with cloud technologies and how you have used them in past projects.
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 when you had to advocate for a technical decision. How did you handle it?
Describe a time when you had to advocate for a technical decision. How did you handle it?
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 prioritize your tasks when working on multiple projects?
How do you prioritize your tasks when working on multiple projects?
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?
Can you provide an example of how you handled conflict within a team?
Can you provide an example of how you handled conflict within a team?
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 your experience with cloud technologies and how you have used them in past projects.
- 02
Describe a time when you had to advocate for a technical decision. How did you handle it?
- 03
How do you prioritize your tasks when working on multiple projects?
- 04
Can you provide an example of how you handled conflict within a team?
How difficult are the interviews at Thorn?
The interviews at Thorn are generally considered to be of average difficulty, with a mix of technical and behavioral questions. Adequate preparation focusing on both aspects will enhance your chances of success.
Thorn Software Engineer candidate reports ↗What differentiates successful candidates?
Successful candidates demonstrate strong technical skills, effective communication, and a genuine passion for Thorn's mission. They also show an ability to collaborate well within teams and adapt to challenges.
Thorn Software Engineer candidate reports ↗How is the culture at Thorn?
Thorn's culture emphasizes collaboration, empathy, and a commitment to social impact. Team members are encouraged to contribute ideas and work together towards a common goal of protecting children.
Thorn Software Engineer candidate reports ↗What is the typical timeline from the initial screen to an offer?
The interview process can take several weeks, depending on the number of candidates and availability of interviewers. It's common to expect feedback within a week after each interview stage.
Thorn Software Engineer candidate reports ↗Are remote work options available?
Thorn offers flexible work arrangements, including remote and hybrid options, depending on the specific team and role requirements.
Thorn Software Engineer candidate reports ↗What topics does Thorn test in interviews?
Thorn interviews most often cover Competitive intelligence, Cold Email Outreach, Communication Skills, Model Building Process, and Technical assessment. The exact emphasis depends on the specific role you apply for.
Thorn 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.
- 01Thorn 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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