As a Software Engineer at Open Systems, you will play a pivotal role in designing, building, and maintaining the core infrastructure that secures global enterprise networks. Specifically working within the Network Detection and Response (NDR) domain, your engineering contributions directly safeguard organizations against sophisticated cyber threats. You will be responsible for developing high-performance, low-latency packet processing engines and intelligent detection systems that analyze massive volumes of network traffic in real time. The work you do here bridges the gap between systems programming, deep networking, and cybersecurity. At Open Systems, software engineering is not just about writing clean code; it is about building highly resilient, scalable systems that can run under intense performance constraints. You will collaborate with threat researchers, security analysts, and fellow systems engineers to turn complex network security challenges into robust, production-grade software. This role is highly critical because the security of hundreds of global enterprises relies on the stability and accuracy of our NDR platform. You will have the opportunity to work with modern systems languages like, dive deep into kernels, and solve intricate concurrency and optimization challenges. It is a stimulating, fast-paced environment where your technical decisions have an immediate, tangible impact on global cybersecurity. Go Unix
Initial Conversation
reportedDiscussion with a recruiter and hiring manager to align on expectations, experience, and cultural fit.
What to demonstrate
- Discussion with a recruiter and hiring manager to align on expectations, experience, and cultural fit
- Depth in Live Coding
How to prepare
- Answer aloud and timed: Explain how a packet travels through the Unix network stack from the physical interface to user space.
- Answer aloud and timed: How would you debug a high-latency issue on a production Unix server using standard command-line utilities?
Technical Evaluation
reportedEngagement in comprehensive live coding sessions and deep-dive technical discussions.
What to demonstrate
- Engagement in comprehensive live coding sessions and deep-dive technical discussions
- Depth in Live Coding
How to prepare
- Answer aloud and timed: What are the differences between TCP and UDP socket programming, and when would you choose one over the other in a high-throughput system?
- Answer aloud and timed: Explain the concept of non-blocking I/O and how multiplexing system calls like
epollwork under the hood.
Real-World Simulation
reportedAssessment of independent problem-solving skills and collaboration through simulated engineering challenges.
What to demonstrate
- Assessment of independent problem-solving skills and collaboration through simulated engineering challenges
- Depth in Live Coding
How to prepare
- Answer aloud and timed: How does the OS handle memory management and context switching during heavy network I/O?
- Answer aloud and timed: Given a small program written in Go, locate the synchronization bug and optimize its execution speed.
PracHub editorial advice for the preparation topics above.
Master Go concurrency and profiling
Before your interview, practice writing concurrent code in Go, and make sure you understand how to use profiling tools to identify memory leaks and CPU bottlenecks.
Expect deep-dive questions on networking protocols
Simply knowing the OSI model is not enough; you must understand socket-level programming, packet headers, and actual TCP/IP behavior under pressure.
Brush up on Unix network utilities
Be ready to discuss how you would use tools like tcpdump, wireshark, netstat, and lsof to diagnose real-world networking and system performance issues.
Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.
How does the OS handle memory management and context switching during heavy network I/O?
How does the OS handle memory management and context switching during heavy network I/O?
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?
Given a small program written in Go, locate the synchronization bug and optimize its execution speed.
Given a small program written in Go, locate the synchronization bug and optimize its execution speed.
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 refactor a memory-intensive data parser in Go to minimize garbage collection overhead?
How would you refactor a memory-intensive data parser in Go to minimize garbage collection overhead?
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?
Implement a thread-safe rate limiter that can handle thousands of concurrent network requests.
Implement a thread-safe rate limiter that can handle thousands of concurrent network requests.
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?
Walk through how you would detect and resolve a race condition in a highly concurrent production application.
Walk through how you would detect and resolve a race condition in a highly concurrent production application.
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?
Given a specific algorithms problem, write a clean, time-optimized solution and explain its computational comp
Given a specific algorithms problem, write a clean, time-optimized solution and explain its computational complexity.
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?
Hold a per-tenant active cap against concurrent creates
A tenant on the standard plan may hold at most 50 resources with status='active'. The create handler runs SELECT count(*) FROM resource WHERE tenant_id = $1 AND status = 'active', compares to 50, then inserts. Two creates arrive 3 ms apart on different instances and the tenant lands at 51. Name the anomaly, say whether PostgreSQL 16 READ COMMITTED or REPEATABLE READ prevents it and why, then give an implementation that holds the cap at READ COMMITTED with the exact statements. Finally, say what changes when the cap is 'at most one running export per tenant' on job_run.
Approach
- Name it: write skew. The two transactions read an overlapping set and write disjoint rows, so there is no row-level conflict for the engine to detect and each commit is individually legal.
- Rule out the levels precisely. READ COMMITTED takes a fresh snapshot per statement and takes no lock on the counted rows, so both see 49. PostgreSQL's REPEATABLE READ is snapshot isolation: it removes non-repeatable reads and phantoms within the snapshot but still admits write skew, because the anomaly is not a re-read of a changed row, it is a read of a set that a concurrent transaction invalidates. Only SERIALIZABLE closes it, by tracking the read dependency and aborting one transaction with SQLSTATE 40001 — a guarantee that exists only if the application re-runs the whole transaction from the read.
- Convert the set predicate into a single-row conflict: keep tenant.active_resource_count and run UPDATE tenant SET active_resource_count = active_resource_count + 1 WHERE tenant_id = $1 AND active_resource_count < 50 in the same transaction as the INSERT. Zero affected rows is the cap, returned as 409. The row lock serialises the decision at any isolation level, and contention is bounded to one tenant's row — which is also the fair-scheduling unit, unlike a global counter that would convoy every tenant behind one row.
- State the cost you just took on: a counter is a second source of truth that can drift, so every path that changes status must adjust it inside the same transaction, and a periodic reconciliation has to exist, with resource_revision as the authority for what the count should have been.
Follow-up
- A resource moves from archived back to active. Which statements change, and what breaks if the counter update and the status change land in different transactions?
- The cap becomes plan-dependent and a plan can change mid-month. Where does the number 50 live, and who reads it?
Write the update path that detects a concurrent edit
resource carries version INT NOT NULL DEFAULT 1. resource_revision holds revision_id, resource_id, version, actor_user_id, change_kind, patch JSONB, request_id, created_at with UNIQUE (resource_id, version). outbox_event holds aggregate_type, aggregate_id, aggregate_version, event_type, payload, status. A PUT carries the version the client read. Write the exact statements for the single transaction that applies the edit, records the revision and enqueues 'resource.updated', and give the handler's branch on zero affected rows. Then say what PostgreSQL 16 does under READ COMMITTED when two of these updates hit one row at once.
Approach
- One transaction, three writes, no network call inside it: UPDATE resource SET title = $3, version = version + 1, updated_at = now() WHERE resource_id = $1 AND tenant_id = $4 AND version = $2; then INSERT the resource_revision row at version $2 + 1; then INSERT the outbox_event row at the same aggregate_version. The event goes to a table rather than a broker because no transaction spans both.
- Branch on the affected-row count before doing anything else. Zero has three causes — stale version, wrong tenant, row gone — so re-read once and map to 409 carrying the current version, or 404 for an id outside the caller's tenant, which also stops the endpoint confirming that another tenant's id exists.
- State the engine behaviour instead of assuming it. Under READ COMMITTED the second UPDATE blocks on the row lock, and when the first commits PostgreSQL re-evaluates the WHERE clause against the newly committed row, so the version predicate now fails and the statement reports zero rows. Under REPEATABLE READ the identical collision raises SQLSTATE 40001 instead, so the handler must fold both shapes into one conflict response.
- Keep UNIQUE (resource_id, version) even though the predicate already serialises writers. It is what makes a lost update unwritable if any other path ever reaches the revision table, and it converts a logic bug into 23505 rather than into a silently missing history row.
Follow-up
- A client sends the version it read ten minutes ago and the resource has moved three versions. What is in your 409 so it can resolve the conflict without a full re-fetch?
- Two editors, two disjoint fields, no overlap. Does your answer still refuse the second write, and should it?
Explain how a packet travels through the Unix network stack from the physical interface to user space.
Explain how a packet travels through the Unix network stack from the physical interface to user space.
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?
What are the differences between TCP and UDP socket programming, and when would you choose one over the other
What are the differences between TCP and UDP socket programming, and when would you choose one over the other in a high-throughput 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?
Explain the concept of non-blocking I/O and how multiplexing system calls like `epoll` work under the hood.
Explain the concept of non-blocking I/O and how multiplexing system calls like epoll work under the hood.
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?
Discuss a recent major cybersecurity event or vulnerability and explain how network-level detection could have
Discuss a recent major cybersecurity event or vulnerability and explain how network-level detection could have mitigated 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 would you design a system to detect DNS tunneling or data exfiltration attempts in real-time traffic?
How would you design a system to detect DNS tunneling or data exfiltration attempts in real-time traffic?
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?
What are the primary indicators of compromise (IoCs) you would look for when analyzing raw network packet capt
What are the primary indicators of compromise (IoCs) you would look for when analyzing raw network packet captures?
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 balance the trade-off between deep packet inspection (DPI) depth and system processing performance?
How do you balance the trade-off between deep packet inspection (DPI) depth and system processing performance?
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 debug a high-latency issue on a production Unix server using standard command-line utilities?
How would you debug a high-latency issue on a production Unix server using standard command-line utilities?
Approach
- Establish what changed and when, before forming any theory.
- Pick a bisection that eliminates candidates whichever way it turns out.
- Check the instrumentation before believing the symptom.
- Separate the trigger from the cause; the deploy is rarely the bug.
Follow-up
- What would you look at first, and what would it rule out?
- How would you tell a cause from a coincidence here?
Built from the rounds and topics Open Systems candidates report.
Prepare, practise & reflect
One practical outcome each day. Spend longer where you need it.
0 / 7 done01Map the Open Systems loop
- Write out the reported sequence: Initial Conversation, Technical Evaluation, Real-World Simulation.
- 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 Live Coding
- Spend the session on Live Coding, which Open Systems candidates report being tested on.
- Write one worked example in Live Coding and time yourself on it.
Deliverable: One timed worked example in Live Coding.
03Work Cybersecurity (Concepts)
- Spend the session on Cybersecurity (Concepts), which Open Systems candidates report being tested on.
- Write one worked example in Cybersecurity (Concepts) and time yourself on it.
Deliverable: One timed worked example in Cybersecurity (Concepts).
04Work Bug Fixing / Debugging
- Spend the session on Bug Fixing / Debugging, which Open Systems candidates report being tested on.
- Write one worked example in Bug Fixing / Debugging and time yourself on it.
Deliverable: One timed worked example in Bug Fixing / Debugging.
05Answer out loud: Unix Systems & Networking
- Answer aloud, timed: Explain how a packet travels through the Unix network stack from the physical interface to user space.
- Answer aloud, timed: How would you debug a high-latency issue on a production Unix server using standard command-line utilities?
Deliverable: Spoken answers to 2 reported Unix Systems & Networking question(s), under time.
06Answer out loud: Coding, Bug Finding & Optimization
- Answer aloud, timed: Given a small program written in Go, locate the synchronization bug and optimize its execution speed.
- Answer aloud, timed: How would you refactor a memory-intensive data parser in Go to minimize garbage collection overhead?
Deliverable: Spoken answers to 2 reported Coding, Bug Finding & Optimization question(s), under time.
07Answer out loud: Cybersecurity & Industry Domain
- Answer aloud, timed: Discuss a recent major cybersecurity event or vulnerability and explain how network-level detection could have mitigated it.
- Answer aloud, timed: How would you design a system to detect DNS tunneling or data exfiltration attempts in real-time traffic?
Deliverable: Spoken answers to 2 reported Cybersecurity & Industry Domain 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?
Estimate work you have never done and defend the range
You are asked to estimate a change you have never attempted: add a column to a 100-million-row table, populate it, move reads across, and drop the old shape. Give a range with the assumptions that generate it, including batch size, the signal your backfill throttles on, and wall-clock hours, and name the three unknowns that would move the number most. Then describe a real estimate you gave under comparable ignorance: how you expressed its uncertainty, what you committed to, and how wrong you turned out to be.
Approach
- Decompose into independently deployable steps before estimating anything: add the column nullable, write both shapes, backfill in batches, verify, move reads, stop writing the old shape, drop it. That is four deploys spread over days, and the calendar estimate is dominated by them rather than by the loop's runtime.
- Do the arithmetic aloud for the part that has arithmetic in it: batch size times number of batches times per-batch duration, at a write rate the primary can absorb alongside roughly 1.2k writes per second of production traffic. The loop is throttled by replication lag and lock waits, not by how fast it can issue statements.
- Price the schema step by its lock rather than its statement duration. In PostgreSQL an ALTER TABLE taking ACCESS EXCLUSIVE waits for every open transaction on that table while later queries queue behind it, so a millisecond change issued during a thirty-second analytics query stalls that table for thirty seconds. Adding a nullable column with a non-volatile default avoids a rewrite from version 11; a new index wants CREATE INDEX CONCURRENTLY, which cannot run inside a transaction block and leaves an invalid index behind if it fails.
- Express the answer as a range whose endpoints each trace to a stated assumption, then name the cheapest experiment that collapses it, which is almost always running one real batch against the real table and multiplying.
Follow-up
- How do you verify the backfill genuinely finished, given rows written by production traffic while it ran?
- Where does the backfill resume from after a worker is killed mid-batch, and what makes that resume point trustworthy?
Argue against a design, lose, and commit anyway
Describe a design you argued against and lost. State the failure you predicted as a named mechanism, not a feeling about complexity: two services that would need one transaction, a projection with no rebuild path, a write path with no idempotency key. Say what evidence you brought, what the decision maker weighed instead, and what you did after the decision was made: what you instrumented, what you wrote down, and whether the prediction came true. Five minutes.
Approach
- State the prediction in falsifiable form up front: the mechanism, the condition that triggers it, and the observable outcome. A prediction that cannot be checked also cannot be credited to you later.
- Show the evidence you had at the time and label each piece honestly as measured, analogous, or intuition. Keeping the intuition is fine; disguising it as data is the thing that erodes your standing in the next argument.
- Represent the opposing case at full strength, including the constraint you did not control: a fixed date, a team boundary, or the fact that the decision was cheap to reverse and yours was not.
- Make disagree-and-commit concrete. Name the artefact you left behind so the prediction could be settled without you: the alert and its threshold, the counter on the dashboard, the decision note that recorded the trade-off and the condition that would revisit it.
Follow-up
- What threshold on that alert would have proved you right, and did anyone ever look at it?
- If the same proposal arrived tomorrow with the same deadline, would you argue it the same way?
- 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
You are asked to estimate a change you have never attempted: add a column to a 100-million-row table, populate it, move reads across, and drop the old shape. Give a range with the assumptions that generate it, including batch size, the signal your backfill throttles on, and wall-clock hours, and name the three unknowns that would move the number most. Then describe a real estimate you gave under comparable ignorance: how you expressed its uncertainty, what you committed to, and how wrong you turned out to be.
- 03
Describe a design you argued against and lost. State the failure you predicted as a named mechanism, not a feeling about complexity: two services that would need one transaction, a projection with no rebuild path, a write path with no idempotency key. Say what evidence you brought, what the decision maker weighed instead, and what you did after the decision was made: what you instrumented, what you wrote down, and whether the prediction came true. Five minutes.
Do I need to have professional experience in Go before applying?
While strong experience in Go is highly preferred, developers with deep systems programming experience in languages like C, C++, or Rust who can quickly master Go syntax and concurrency models are also highly competitive. However, you should expect to be evaluated on Go-specific code optimizations during the technical rounds.
Open Systems Software Engineer candidate reports ↗How difficult is the technical interview process?
The process is considered average to difficult, depending on your familiarity with network protocols and systems-level programming. The interviewers go in-depth and test your ability to perform under pressure, but they are also very supportive and collaborative throughout the sessions.
Open Systems Software Engineer candidate reports ↗Is relocation assistance provided for roles in Zürich?
Yes, Open Systems is highly supportive of relocation efforts and has a reputation for being very helpful and quick to assist successful candidates with the practicalities of moving to Zürich.
Open Systems Software Engineer candidate reports ↗What distinguishes a successful candidate from an average one?
Successful candidates demonstrate a deep curiosity about how systems work under the hood. They don't just write code that works; they understand its impact on CPU, memory, and network resources, and they demonstrate a strong interest in modern cybersecurity challenges.
Open Systems Software Engineer candidate reports ↗What topics does Open Systems test in interviews?
Open Systems interviews most often cover Linux, Kubernetes (k8s), DevOps Practices, Problem Solving, and Teamwork / Collaboration. The exact emphasis depends on the specific role you apply for.
Open Systems 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.
- 01Open Systems 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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