At Ontic, a Software Engineer plays a pivotal role in building and maintaining the industry-leading protective intelligence platform. The platform is trusted by major enterprises to keep their people, assets, and operations safe from physical security threats. As a member of the engineering team, you will design, develop, and scale real-time data pipelines, complex integrations, and highly responsive user interfaces that consolidate disparate threat data into a single, actionable pane of glass. The impact of your work in this role cannot be overstated. Unlike traditional enterprise software, the systems you build at Ontic directly influence physical safety and incident mitigation. This requires engineering solutions with an uncompromising focus on reliability, low latency, and security. You will work on sophisticated database structures, geospatial indexing, and real-time alerting systems that must perform flawlessly under pressure. To succeed as a Software Engineer at Ontic, you must possess a strong foundational grasp of computer science principles, a passion for clean low-level design, and the ability to optimize complex database queries. You will collaborate closely with product managers, security analysts, and platform engineers to transform complex threat-monitoring workflows into elegant, robust software solutions.
Screening Phase
reportedInitial assessment including offline coding and aptitude tests with logical questions and coding challenges.
What to demonstrate
- Initial assessment including offline coding and aptitude tests with logical questions and coding challenges
- Depth in Data Structures & Algorithms (DSA)
How to prepare
- Answer aloud and timed: Given a large stream of log data, how would you implement a sliding window to detect anomalies using a stack or queue?
- Answer aloud and timed: Implement a graph traversal algorithm (such as BFS) combined with binary search to find the shortest path in a weighted network.
Technical Rounds
reportedIn-depth interviews covering computer science fundamentals, practical coding, systems design, and behavioral fit.
What to demonstrate
- In-depth interviews covering computer science fundamentals, practical coding, systems design, and behavioral fit
- Depth in Data Structures & Algorithms (DSA)
How to prepare
- Answer aloud and timed: Solve a classic dynamic programming problem, such as finding the longest common subsequence or calculating the minimum path sum in a grid.
- Answer aloud and timed: Write an efficient algorithm to manipulate, search, or pattern-match complex string data without using built-in library functions.
PracHub editorial advice for the preparation topics above.
Master the Basics
Do not skip studying computer networks, database indexing, and operating system concepts. Interviewers frequently drill down into how your code interacts with the network and OS layers.
Practice Low-Level Coding
Practice writing clean, object-oriented code for common design problems (like a parking lot, movie theater, or elevator system) on paper or a whiteboard. Focus on clean class structures and solid design patterns.
Think Out Loud
During technical and puzzle-solving rounds, communicate your thought process clearly. The interviewer is often more interested in how you approach a problem and handle feedback than whether you get the perfect answer immediately.
Be Ready to Discuss Your Projects
Be prepared to explain the architecture, trade-offs, and challenges of the projects listed on your resume. You must show a deep understanding of the technologies you have used in the past.
Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.
Given a large stream of log data, how would you implement a sliding window to detect anomalies using a stack o
Given a large stream of log data, how would you implement a sliding window to detect anomalies using a stack or queue?
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 a graph traversal algorithm (such as BFS) combined with binary search to find the shortest path in a
Implement a graph traversal algorithm (such as BFS) combined with binary search to find the shortest path in a weighted network.
Approach
- Restate the input: its shape, its size, and what is guaranteed about it.
- Name the brute-force solution and its complexity before improving on it.
- Choose the data structure from the access pattern, not from familiarity.
- State the target complexity and say which constraint rules the naive version out.
Follow-up
- How does this change if the input no longer fits in memory?
- What is the worst case, and how likely is it on real data?
Solve a classic dynamic programming problem, such as finding the longest common subsequence or calculating the
Solve a classic dynamic programming problem, such as finding the longest common subsequence or calculating the minimum path sum in a grid.
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?
Write an efficient algorithm to manipulate, search, or pattern-match complex string data without using built-i
Write an efficient algorithm to manipulate, search, or pattern-match complex string data without using built-in library functions.
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 does the Linux kernel manage process scheduling and memory allocation? Explain the concept of virtual memo
How does the Linux kernel manage process scheduling and memory allocation? Explain the concept of virtual memory.
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?
How would you diagnose a high-CPU or memory-leak issue in a live production environment running on a Linux ser
How would you diagnose a high-CPU or memory-leak issue in a live production environment running on a Linux server?
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 how you would optimize a slow-running SQL query that involves multiple joins over tables with millions
Explain how you would optimize a slow-running SQL query that involves multiple joins over tables with millions of rows.
Approach
- Name the grain you start from and join outward from it.
- Check whether any join is one-to-many before aggregating, or the sums inflate.
- Say which index the query would use, and what makes it unusable.
- 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?
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?
Design the database schema and backend architecture for a platform like LinkedIn, focusing on connection track
Design the database schema and backend architecture for a platform like LinkedIn, focusing on connection tracking and fast feed generation.
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?
Create a low-level design (LLD) for a parking lot or a movie ticket booking system, and write the actual code
Create a low-level design (LLD) for a parking lot or a movie ticket booking system, and write the actual code implementing the core classes and 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?
How would you structure a database to support fast, real-time spatial queries for threat events occurring with
How would you structure a database to support fast, real-time spatial queries for threat events occurring within a specific radius?
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 the difference between TCP and UDP, and describe how you would choose between them for a real-time ale
Explain the difference between TCP and UDP, and describe how you would choose between them for a real-time alerting system.
Approach
- Fix the scope first: who calls this, how often, and what they do when it fails.
- Name the read and write paths separately; they rarely have the same bottleneck.
- Choose a partition key and say what query it makes expensive.
- State the consistency you need, and where you are willing to be stale.
Follow-up
- What breaks first when traffic grows ten times?
- How does this behave when that dependency is down for an hour?
Describe what happens under the hood when a client makes an HTTPS request to an API gateway.
Describe what happens under the hood when a client makes an HTTPS request to an API gateway.
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?
You have 8 identical-looking balls, one of which is slightly heavier than the rest. Using a balance scale, wha
You have 8 identical-looking balls, one of which is slightly heavier than the rest. Using a balance scale, what is the minimum number of weighings required to find the heavier ball?
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?
Explain how you would measure exactly 4 liters of water using only a 3-liter jug and a 5-liter jug.
Explain how you would measure exactly 4 liters of water using only a 3-liter jug and a 5-liter jug.
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?
Every query on one table stalls for forty seconds mid-deploy
During a release on PostgreSQL, every query touching resource times out for about 40 seconds and then recovers with no intervention. The release ran one migration, ALTER TABLE resource ADD COLUMN archived_reason TEXT, and the migration log shows it completing in 6 ms. Unrelated tables showed no change in error rate. Explain how a 6 ms statement caused a 40-second stall, give the ordered checks you would run on a live system to confirm it, and give the migration procedure that prevents a repeat.
Approach
- Separate the statement's duration from the lock's duration. ADD COLUMN with no default is a catalogue-only change and genuinely runs in milliseconds, but it requires ACCESS EXCLUSIVE, and it cannot acquire that until every transaction already touching the table has finished.
- Account for the queueing, which is the part that surprises people. A lock request that is waiting blocks later requests for conflicting modes behind it rather than letting them overtake, so one long-open transaction holds the DDL and the DDL holds all the traffic. The stall length is set by the longest open transaction, not by the size of the change.
- Confirm on a live system in this order: pg_stat_activity for that table ordered by xact_start, looking for the oldest transaction and specifically for state = idle in transaction; then pg_locks where granted = false to find the waiter; then join them on pid to name blocker and blocked. pg_blocking_pids() does that join for you and is the fastest single call.
- Prevent rather than merely time it better. Set lock_timeout to a second or two on the migration session so the DDL abandons the queue after a bounded wait and is retried, instead of holding it for as long as the oldest transaction lives. Be exact about what that buys: queries arriving during the wait still queue behind the pending ACCESS EXCLUSIVE request, so each attempt costs them up to one lock_timeout of added latency. The outage goes from 40 seconds to about one second per attempt, not to zero. Also run migrations away from deploy-time peaks, and put a statement timeout and an idle-in-transaction timeout on the analytics role that opens the long transactions.
Follow-up
- The same release also wants NOT NULL on that column. What is the sequence that gets there without a long lock?
- Your lock_timeout retry fails ten times in a row because the analytics transaction is always open. What do you change?
Built from the rounds and topics Ontic candidates report.
Prepare, practise & reflect
One practical outcome each day. Spend longer where you need it.
0 / 7 done01Map the Ontic loop
- Write out the reported sequence: Screening Phase, Technical Rounds.
- For each round, write one sentence on what it is judging, from the description above, and mark the one you are least ready for.
Deliverable: A one-page map of the 2 reported rounds, with the weakest marked.
02Work Data Structures & Algorithms (DSA)
- Spend the session on Data Structures & Algorithms (DSA), which Ontic 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 Problem Solving
- Spend the session on Problem Solving, which Ontic candidates report being tested on.
- Write one worked example in Problem Solving and time yourself on it.
Deliverable: One timed worked example in Problem Solving.
04Work Computer Networks
- Spend the session on Computer Networks, which Ontic candidates report being tested on.
- Write one worked example in Computer Networks and time yourself on it.
Deliverable: One timed worked example in Computer Networks.
05Answer out loud: Algorithmic Problem Solving
- Answer aloud, timed: Given a large stream of log data, how would you implement a sliding window to detect anomalies using a stack or queue?
- Answer aloud, timed: Implement a graph traversal algorithm (such as BFS) combined with binary search to find the shortest path in a weighted network.
Deliverable: Spoken answers to 2 reported Algorithmic Problem Solving question(s), under time.
06Answer out loud: System & Database Design
- Answer aloud, timed: Design the database schema and backend architecture for a platform like LinkedIn, focusing on connection tracking and fast feed generation.
- Answer aloud, timed: Create a low-level design (LLD) for a parking lot or a movie ticket booking system, and write the actual code implementing the core classes and design patterns.
Deliverable: Spoken answers to 2 reported System & Database Design question(s), under time.
07Answer out loud: Systems Programming & Infrastructure
- Answer aloud, timed: Explain the difference between TCP and UDP, and describe how you would choose between them for a real-time alerting system.
- Answer aloud, timed: How does the Linux kernel manage process scheduling and memory allocation? Explain the concept of virtual memory.
Deliverable: Spoken answers to 2 reported Systems Programming & Infrastructure 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.
Ship under a deadline and bound the debt you chose
You have four days to ship a tenant-facing listing endpoint. The version you would defend uses keyset pagination over (tenant_id, status, updated_at DESC, resource_id DESC); the version you can finish uses LIMIT/OFFSET with no matching index. Describe a deadline call you actually made of this shape: what you shipped, what you knowingly deferred, how you bounded the damage with a mechanism rather than an intention, and the specific numeric condition that would force the follow-up. Name who you told and where you wrote it down.
Approach
- Name the deferred failure precisely instead of calling it slow. OFFSET n makes the database produce and discard n rows, so cost grows with page depth; without an index matching the sort, every matching row is read and sorted before the limit applies; and rows inserted between two page fetches shift across the boundary so items are skipped or repeated with nothing in the response to signal it.
- Bound the blast radius with something mechanical rather than a promise: cap maximum page depth, cap page size, restrict the endpoint to one internal caller, or keep it behind a flag. State which failure each cap removes and which it leaves standing.
- Attach a number to the trigger and wire it to an alarm: the first tenant crossing N resources, or the endpoint's p99 crossing its share of the 400 ms budget, so the debt announces itself instead of waiting to be remembered.
- Write it where the next engineer looks, which is the code and the ticket, not a chat message: what was deferred, why, the cap, and the trigger.
Follow-up
- At what page depth does the offset version breach your latency budget, given your page size and row counts?
- What breaks first when you switch to keyset pagination later, and what does a client holding an old page token see?
Turn a code review disagreement into a decision
A colleague's change updates a row with UPDATE resource SET version = version + 1 WHERE resource_id = $1 AND version = $2 and treats an affected-row count of zero as a successful no-op. You read that as a silently lost update; they think returning 200 is friendlier to clients than returning a conflict. Describe how you have handled a review disagreement of this shape: what goes in the comment, when you leave the thread, and who decides. Then write the comment you would leave here, in under 80 words.
Approach
- Sort the disagreement before writing anything. A silently discarded write is a correctness claim about data; the choice between 409 and 412 is taste. Only the first justifies blocking a merge, and saying which one you are doing is most of the value of the comment.
- Make the claim reproducible in the comment itself with an interleaving rather than a principle: A reads version 7, B reads version 7, B commits version 8, A's predicate matches zero rows, A is told it succeeded and A's edit is gone.
- Offer the alternative with its cost attached: return 409 carrying the current version and the revision that won, so the client can re-read and re-apply. Note that automatic retry is not the fix, because a retry re-reads the winner's state and reapplies an intent formed against data that no longer exists.
- Apply an escalation rule you can state: two round trips on the thread, then a call, and the service's owner decides rather than the reviewer. A reviewer who cannot be overruled is a bottleneck with extra steps.
Follow-up
- Where would you put the test that fails if someone reintroduces the swallowed zero rowcount?
- The author says clients cannot handle a 409. How do you check whether that is true?
Tell callers you do not own that their integration breaks
A field in a write endpoint's response must change shape. You own the endpoint; you do not own the four internal callers or the outbound webhook consumers who read it. Describe a deprecation you were responsible for: what you shipped first, how you established who was actually reading the field, the window you gave and what set its length, what you did about the consumer who never moved, and how you decided removal was safe. Name the signal you used, not the announcement you sent.
Approach
- Establish the reader set empirically rather than from a wiki of owners: per-field usage counters keyed by principal, or access logs attributed to a consumer. State the blind spot of whichever you pick, since a consumer that reads the field only on a monthly job will not appear in a week of logs.
- Ship additive first. Populate the new field alongside the old one so no reader is forced to move, which is also what keeps a rolling deploy safe, because old and new instances answer the same requests at the same time and a rollback must still find the old shape present.
- Set the window from the slowest legitimate consumer's release cadence, not from your calendar, and decide separately what to do for a consumer with no release process at all, such as an external webhook endpoint you can only email.
- Convert silence into evidence before you rely on it: a short, low-traffic removal window that makes a still-dependent consumer fail visibly and loudly while you are watching, rather than at three in the morning after you have moved on.
Follow-up
- How would you detect a consumer that reads the field only during a monthly export?
- One caller refuses to move and has a commercial relationship behind it. What changes in your plan and what does not?
- 01
You have four days to ship a tenant-facing listing endpoint. The version you would defend uses keyset pagination over (tenant_id, status, updated_at DESC, resource_id DESC); the version you can finish uses LIMIT/OFFSET with no matching index. Describe a deadline call you actually made of this shape: what you shipped, what you knowingly deferred, how you bounded the damage with a mechanism rather than an intention, and the specific numeric condition that would force the follow-up. Name who you told and where you wrote it down.
- 02
A colleague's change updates a row with UPDATE resource SET version = version + 1 WHERE resource_id = $1 AND version = $2 and treats an affected-row count of zero as a successful no-op. You read that as a silently lost update; they think returning 200 is friendlier to clients than returning a conflict. Describe how you have handled a review disagreement of this shape: what goes in the comment, when you leave the thread, and who decides. Then write the comment you would leave here, in under 80 words.
- 03
A field in a write endpoint's response must change shape. You own the endpoint; you do not own the four internal callers or the outbound webhook consumers who read it. Describe a deprecation you were responsible for: what you shipped first, how you established who was actually reading the field, the window you gave and what set its length, what you did about the consumer who never moved, and how you decided removal was safe. Name the signal you used, not the announcement you sent.
How difficult is the Software Engineer interview process at Ontic?
The process is generally rated as moderate to difficult. It requires a strong grasp of core computer science fundamentals (OS, Networking, DBMS) rather than just memorized Leetcode patterns. You must be prepared to write actual compilable code for low-level design questions and solve logical puzzles.
Ontic Software Engineer candidate reports ↗What is the typical timeline from the initial application to an offer?
The interview pipeline typically takes between two to four weeks. This includes the initial screening test, scheduling the technical rounds, and the final managerial/HR discussions. ##### Tip Ensure you maintain active communication with your recruiter throughout the process. If you do not hear back within 3-5 business days after a round, send a polite follow-up email to keep your candidacy moving forward.
Ontic Software Engineer candidate reports ↗Does Ontic focus more on High-Level Design (HLD) or Low-Level Design (LLD)?
While high-level architecture is important, Ontic places a distinct emphasis on Low-Level Design (LLD). You should expect to write actual code implementing your classes, interfaces, and design patterns during the design interviews.
Ontic Software Engineer candidate reports ↗What is the engineering culture like at Ontic?
The engineering culture is highly collaborative, quality-focused, and mission-driven. Because the software is used for protective intelligence and physical security, there is a strong sense of shared responsibility for system reliability, performance, and data security.
Ontic Software Engineer candidate reports ↗What topics does Ontic test in interviews?
Ontic interviews most often cover Problem Solving, Data Structures & Algorithms (DSA), Customer Success Management, Role-Play / Live Sales Simulation, and Sales Presentations. The exact emphasis depends on the specific role you apply for.
Ontic 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.
- 01Ontic 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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