At Seven Ai, the Software Engineer role is the backbone of our mission to build scalable, intelligent systems that push the boundaries of what is possible in the AI landscape. You will not just be writing code; you will be architecting the foundational infrastructure that powers our most critical products. Whether you are operating as a Senior Platform Engineer, Software Architect, or Principal Software Engineer (Cloud), your work directly influences the speed, reliability, and innovation capacity of our entire engineering organization. This role requires a rare blend of deep technical rigor and strategic foresight. You will work within cross-functional teams to solve complex problems, ranging from optimizing distributed cloud architectures to designing product-facing features that millions of users interact with daily. We prioritize engineers who can navigate ambiguity, mentor junior talent, and maintain a high standard of code quality while delivering at the pace of an evolving industry. If you are someone who thrives on building systems that scale and enjoys tackling "hard" engineering problems, you will find a challenging and rewarding environment here.
Technical Screen
reportedInitial assessment to evaluate technical skills and knowledge.
What to demonstrate
- Initial assessment to evaluate technical skills and knowledge
- Depth in Software Engineering
How to prepare
- Answer aloud and timed: How would you design a highly available, distributed system to handle [x] requests per second?
- Answer aloud and timed: Describe the trade-offs between choosing a SQL vs. NoSQL database for a real-time data processing pipeline.
Deep-Dive Technical Rounds
reportedIn-depth technical interviews to assess problem-solving and coding abilities.
What to demonstrate
- In-depth technical interviews to assess problem-solving and coding abilities
- Depth in Software Engineering
How to prepare
- Answer aloud and timed: How do you ensure data consistency in a microservices architecture?
- Answer aloud and timed: Walk me through how you would architect a scalable cloud-native solution for an AI-driven product.
Final Leadership Interviews
reportedInterviews with leadership to evaluate cultural fit and team integration.
What to demonstrate
- Interviews with leadership to evaluate cultural fit and team integration
- Depth in Software Engineering
How to prepare
- Answer aloud and timed: How would you approach a situation where a system's latency has spiked unexpectedly in production?
- Answer aloud and timed: Explain the time and space complexity of your solution; how could it be further optimized?
PracHub editorial advice for the preparation topics above.
Structure your answers
Use the STAR method (Situation, Task, Action, Result) for behavioral questions to keep your responses concise and impactful.
Be transparent about trade-offs
Whenever you make a design choice, explicitly mention the "why." Acknowledge the downsides of your chosen approach; this demonstrates maturity and deep technical understanding.
Ask meaningful questions
Use your time at the end of the interview to ask about the team’s current technical challenges or the company’s long-term engineering vision.
Clarify early
If an interview question seems vague, take a moment to ask clarifying questions. It shows you are thoughtful and value alignment.
Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.
Explain the time and space complexity of your solution; how could it be further optimized?
Explain the time and space complexity of your solution; how could it be further optimized?
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?
Archive a resource graph without breaking live references or recursing
Resources reference other resources within a tenant; for the largest tenant the reference table holds up to 2,000,000 nodes and 8,000,000 edges. Archiving a resource must archive everything reachable from it that nothing outside the set still references, refuse when a live external referrer exists, and terminate when references form cycles, which they legitimately do. Produce the archive order and the refusal list, targeting O(V+E). Say what stops the traversal crossing a tenant boundary, and why recursion is the wrong control structure at this size.
Approach
- Load the subgraph with the tenant predicate on both endpoints of the edge, not only on the side you started from. Scoping the left table alone is the classic cross-tenant leak: one mis-entered edge then pulls another tenant's resources into the traversal and, worse, into the archive.
- Traverse iteratively with an explicit stack. A 2,000,000-node graph can hold a chain deep enough to exhaust a native stack in the low tens of thousands of frames, and that failure is a process crash rather than an error you can return.
- Treat cycles as data rather than corruption: compute strongly connected components with Tarjan in O(V+E) using its own explicit stack, then condense. The condensation is a DAG, so a topological order over it gives the archive order, and every member of a component archives in one transaction because no order within a cycle is valid.
- Decide refusals with reverse edges. A candidate is archivable only if every in-edge originates inside the candidate set, so build the transpose or count in-degrees restricted to the visited set, and emit each blocked resource with the id of the external referrer, which is the only part of the answer an operator can act on.
Follow-up
- The graph is read in one query and the archive writes a minute later. What can change in between, and how do you make the write safe?
- The candidate set is 400,000 resources. Is that one transaction, and if not, what does a half-finished archive look like to a reader?
Canonicalise a request body into a stable idempotency fingerprint
idempotency_key.request_fingerprint is a SHA-256 over the method, path and canonicalised body, and a retry whose fingerprint differs must be rejected with 422 rather than served the stored response. Write the canonicaliser. Bodies are JSON up to 256 KB nested at most 32 levels; clients vary key order, whitespace and unicode escaping, and some send 64-bit ids as JSON numbers. Produce a deterministic byte string such that semantically identical bodies match and any semantic difference does not. State your complexity and name two normalisations you refuse to perform.
Approach
- Parse once into a tree, then re-serialise under fixed rules: object keys sorted, array order preserved, one escaping convention, no insignificant whitespace. Parsing is O(n) and sorting keys is O(k log k) per object, so O(n log n) overall with O(depth) stack, and the 32-level cap is enforced during parsing because hostile nesting is how a canonicaliser becomes a stack overflow.
- Sort keys by their UTF-8 bytes and say why the obvious implementation is wrong in some runtimes: a default string comparison that orders by UTF-16 code units places surrogate pairs, meaning code points from U+10000 up, below U+E000 to U+FFFF, which is not UTF-8 byte order, so two services written in different languages disagree on the same document.
- Do not re-encode numbers through a double. IEEE-754 binary64 represents integers exactly only up to 2^53, so normalising a 19-digit id through a float changes it, and 1 against 1.0 cannot be reconciled without deciding whether they are the same value. Preserve the literal token, and require ids as strings at the API boundary if you want them comparable.
- Reject duplicate keys rather than picking one. JSON permits them and parsers disagree, most keeping the last, so any choice you make ties the fingerprint to a parser detail that the code handling the request does not necessarily share.
Follow-up
- A client sends the same logical request with an extra field your API ignores. Same key, different fingerprint, so you return 422. Is that the right answer?
- Where does the fingerprint get computed relative to request decompression and the body-size limit?
Describe the trade-offs between choosing a SQL vs. NoSQL database for a real-time data processing pipeline.
Describe the trade-offs between choosing a SQL vs. NoSQL database for a real-time data processing pipeline.
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?
How would you design a highly available, distributed system to handle [x] requests per second?
How would you design a highly available, distributed system to handle [x] requests per second?
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 do you ensure data consistency in a microservices architecture?
How do you ensure data consistency in a microservices architecture?
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?
Walk me through how you would architect a scalable cloud-native solution for an AI-driven product.
Walk me through how you would architect a scalable cloud-native solution for an AI-driven product.
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 approach a situation where a system's latency has spiked unexpectedly in production?
How would you approach a situation where a system's latency has spiked unexpectedly in production?
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?
Given a specific API constraint, how would you design a client-side library to interact with our backend?
Given a specific API constraint, how would you design a client-side library to interact with our backend?
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 is your strategy for unit and integration testing in a CI/CD environment?
What is your strategy for unit and integration testing in a CI/CD environment?
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?
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 Seven Ai candidates report.
Prepare, practise & reflect
One practical outcome each day. Spend longer where you need it.
0 / 7 done01Map the Seven Ai loop
- Write out the reported sequence: Technical Screen, Deep-Dive Technical Rounds, Final Leadership 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 Software Engineering
- Spend the session on Software Engineering, which Seven Ai candidates report being tested on.
- Write one worked example in Software Engineering and time yourself on it.
Deliverable: One timed worked example in Software Engineering.
03Work Cloud Engineering
- Spend the session on Cloud Engineering, which Seven Ai candidates report being tested on.
- Write one worked example in Cloud Engineering and time yourself on it.
Deliverable: One timed worked example in Cloud Engineering.
04Work Platform Engineering
- Spend the session on Platform Engineering, which Seven Ai candidates report being tested on.
- Write one worked example in Platform Engineering and time yourself on it.
Deliverable: One timed worked example in Platform Engineering.
05Answer out loud: Systems Design and Architecture
- Answer aloud, timed: How would you design a highly available, distributed system to handle [x] requests per second?
- Answer aloud, timed: Describe the trade-offs between choosing a SQL vs. NoSQL database for a real-time data processing pipeline.
Deliverable: Spoken answers to 2 reported Systems Design and Architecture question(s), under time.
06Answer out loud: Technical Problem Solving
- Answer aloud, timed: Explain the time and space complexity of your solution; how could it be further optimized?
- Answer aloud, timed: Describe a time you had to refactor a legacy codebase to improve performance or maintainability.
Deliverable: Spoken answers to 2 reported Technical Problem Solving question(s), under time.
07Answer out loud: Leadership and Behavioral
- Answer aloud, timed: Tell me about a time you had a technical disagreement with a peer or stakeholder; how did you resolve it?
- Answer aloud, timed: Describe a project where you had to pivot your technical approach due to changing business requirements.
Deliverable: Spoken answers to 2 reported Leadership and Behavioral 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 you had to refactor a legacy codebase to improve performance or maintainability.
Describe a time you had to refactor a legacy codebase to improve performance or maintainability.
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 handle failure modes in distributed systems?
How do you handle failure modes in distributed systems?
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?
Tell me about a time you had a technical disagreement with a peer or stakeholder; how did you resolve it?
Tell me about a time you had a technical disagreement with a peer or stakeholder; how did you resolve 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?
Describe a project where you had to pivot your technical approach due to changing business requirements.
Describe a project where you had to pivot your technical approach due to changing business 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?
How do you balance the need for rapid feature delivery with the need to pay down technical debt?
How do you balance the need for rapid feature delivery with the need to pay down technical debt?
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 share an example of how you have mentored a junior engineer or improved team processes?
Can you share an example of how you have mentored a junior engineer or improved team processes?
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 you had to refactor a legacy codebase to improve performance or maintainability.
- 02
How do you handle failure modes in distributed systems?
- 03
Tell me about a time you had a technical disagreement with a peer or stakeholder; how did you resolve it?
- 04
Describe a project where you had to pivot your technical approach due to changing business requirements.
How long does the interview process typically take?
Most candidates complete the entire process within 3–5 weeks, depending on scheduling availability. We aim to keep the process moving efficiently while ensuring you have enough time to meet with all relevant team members.
Seven Ai Software Engineer candidate reports ↗What is the biggest differentiator for successful candidates?
The most successful candidates are those who demonstrate "systems thinking." They don't just solve the coding problem; they consider the operational, security, and scalability implications of their design choices.
Seven Ai Software Engineer candidate reports ↗Is the interview process mostly coding or system design?
For our senior and principal roles, the emphasis is heavily weighted toward system design and architectural decision-making. You will still face coding challenges, but they are often framed within the context of larger, real-world engineering problems. Do not attempt to memorize solutions to common industry problems. Our interviewers value original thought and the ability to adapt to constraints.
Seven Ai Software Engineer candidate reports ↗What topics does Seven Ai test in interviews?
Seven Ai interviews most often cover Security Engineering, Incident Response, Cross-functional Collaboration, Authentication & Authorization, and Data Modeling. The exact emphasis depends on the specific role you apply for.
Seven Ai 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.
- 01Seven Ai 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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