As a Software Engineer at Manex AI, you are at the intersection of cutting-edge artificial intelligence and high-impact product engineering. Your work is fundamental to building the core infrastructure and application layers that enable Manex AI to solve complex, real-world problems for its clients. You will not just be writing code; you will be architecting scalable systems that translate theoretical AI models into tangible, user-centric solutions. This role is critical to the company’s mission of driving measurable impact. Whether you are working on the foundational backend services or developing specialized interfaces, you will be expected to balance technical rigor with rapid iteration. The environment is fast-paced, intellectually demanding, and offers the unique opportunity to see your contributions directly influence the product roadmap and user outcomes.
Initial Technical Screens
reportedCandidates undergo initial technical assessments to evaluate their coding skills.
What to demonstrate
- Candidates undergo initial technical assessments to evaluate their coding skills
- Depth in Problem Solving
How to prepare
- Answer aloud and timed: Describe a time you had to optimize a piece of code that was causing performance bottlenecks.
- Answer aloud and timed: How do you approach debugging a distributed system error that is difficult to reproduce?
Final Assessment Rounds
reportedCandidates participate in final rounds that may include collaborative design discussions.
What to demonstrate
- Candidates participate in final rounds that may include collaborative design discussions
- Depth in Problem Solving
How to prepare
- Answer aloud and timed: Explain the trade-offs between different data structures for a high-concurrency application.
- Answer aloud and timed: What is your strategy for unit testing and ensuring code quality in a fast-moving environment?
PracHub editorial advice for the preparation topics above.
Think out loud
During coding and design rounds, explain your thought process. It helps the interviewer understand your logic and allows them to guide you if you get stuck.
Ask clarifying questions
Don't rush into a solution. Clarify requirements and constraints first to ensure you are solving the right problem.
Focus on trade-offs
In system design, there is rarely one "perfect" answer. Acknowledge the trade-offs of your proposed solution regarding speed, cost, and complexity.
Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.
Explain the trade-offs between different data structures for a high-concurrency application.
Explain the trade-offs between different data structures for a high-concurrency 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?
What is your strategy for unit testing and ensuring code quality in a fast-moving environment?
What is your strategy for unit testing and ensuring code quality in a fast-moving environment?
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?
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?
Compare the pros and cons of SQL versus NoSQL databases for a specific use case.
Compare the pros and cons of SQL versus NoSQL databases for a specific use case.
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?
Find version gaps and relay lag with window functions
outbox_event holds event_id, aggregate_type, aggregate_id, aggregate_version, event_type, payload, status ('pending','published','dead'), attempts, created_at, published_at. A projection is missing rows and you must decide whether the relay skipped events or the consumer dropped them. Write three queries over the last seven days: one listing every aggregate_id whose published aggregate_version sequence has a hole, one giving per-day counts with a running total, and one returning the newest published event per aggregate. For each, say where the window function is evaluated relative to WHERE and LIMIT. PostgreSQL 16.
Approach
- Gaps: compute lead(aggregate_version) OVER (PARTITION BY aggregate_id ORDER BY aggregate_version) in a subquery, then filter next_version <> aggregate_version + 1 in the outer query. Window functions are evaluated after WHERE, GROUP BY and HAVING and before the outer ORDER BY and LIMIT, so the predicate cannot sit in the same WHERE clause and PostgreSQL 16 has no QUALIFY.
- Say what the seven-day filter does to the answer: it truncates every partition, so the first row per aggregate has no predecessor inside the window and a hole spanning the boundary is invisible. Widen the window, or join to resource.version as the authority for the true maximum.
- Running total: SELECT date_trunc('day', created_at) AS d, count() AS n, sum(count()) OVER (ORDER BY date_trunc('day', created_at) ROWS UNBOUNDED PRECEDING). An aggregate inside a window call is legal because grouping runs before windowing. The grouping key is unique per row here so ROWS and RANGE agree, but write the frame anyway — over ungrouped rows with tied timestamps the default RANGE frame pulls in every peer row and the total jumps.
- Newest per aggregate: DISTINCT ON (aggregate_id) ... ORDER BY aggregate_id, aggregate_version DESC is the cheap PostgreSQL-only form when an index matches that order; row_number() OVER (PARTITION BY aggregate_id ORDER BY aggregate_version DESC) = 1 is the portable form and needs a subquery for the same evaluation-order reason as the gap query.
Follow-up
- Relay failover redelivers events. Does a duplicate break the gap query, and how would you detect one from this table alone?
- Turn the gap check into a continuous monitor rather than a query someone runs after an incident. What does it watch?
How do you handle API versioning when breaking changes are required?
How do you handle API versioning when breaking changes are required?
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?
How would you design a real-time data processing pipeline?
How would you design a real-time data processing pipeline?
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 considerations when scaling a database for a global user base?
What are the primary considerations when scaling a database for a global user base?
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 high availability and fault tolerance in a microservices architecture?
How do you ensure high availability and fault tolerance 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?
How do you secure internal services against unauthorized access?
How do you secure internal services against unauthorized access?
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 approach debugging a distributed system error that is difficult to reproduce?
How do you approach debugging a distributed system error that is difficult to reproduce?
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 Manex AI candidates report.
Prepare, practise & reflect
One practical outcome each day. Spend longer where you need it.
0 / 7 done01Map the Manex AI loop
- Write out the reported sequence: Initial Technical Screens, Final Assessment 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 Problem Solving
- Spend the session on Problem Solving, which Manex AI 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.
03Work Programming Fundamentals
- Spend the session on Programming Fundamentals, which Manex AI candidates report being tested on.
- Write one worked example in Programming Fundamentals and time yourself on it.
Deliverable: One timed worked example in Programming Fundamentals.
04Work Software Engineering (General)
- Spend the session on Software Engineering (General), which Manex AI candidates report being tested on.
- Write one worked example in Software Engineering (General) and time yourself on it.
Deliverable: One timed worked example in Software Engineering (General).
05Answer out loud: Technical Foundations and Coding
- Answer aloud, timed: Describe a time you had to optimize a piece of code that was causing performance bottlenecks.
- Answer aloud, timed: How do you approach debugging a distributed system error that is difficult to reproduce?
Deliverable: Spoken answers to 2 reported Technical Foundations and Coding question(s), under time.
06Answer out loud: System Design and Architecture
- Answer aloud, timed: How would you design a real-time data processing pipeline?
- Answer aloud, timed: What are the primary considerations when scaling a database for a global user base?
Deliverable: Spoken answers to 2 reported System Design and Architecture question(s), under time.
07Answer out loud: Behavioral and Problem-Solving
- Answer aloud, timed: Tell me about a time you disagreed with a technical decision made by your lead. How did you handle it?
- Answer aloud, timed: Describe a project where you had to pivot quickly due to changing requirements.
Deliverable: Spoken answers to 2 reported Behavioral and Problem-Solving 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 optimize a piece of code that was causing performance bottlenecks.
Describe a time you had to optimize a piece of code that was causing performance bottlenecks.
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 disagreed with a technical decision made by your lead. How did you handle it?
Tell me about a time you disagreed with a technical decision made by your lead. 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?
Describe a project where you had to pivot quickly due to changing requirements.
Describe a project where you had to pivot quickly due to changing 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 prioritize your tasks when you have multiple competing deadlines?
How do you prioritize your tasks when you have multiple competing deadlines?
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?
Give an example of a time you mentored a junior engineer or improved team documentation.
Give an example of a time you mentored a junior engineer or improved team documentation.
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?
What attracts you to the challenges we are solving at Manex AI?
What attracts you to the challenges we are solving at Manex AI?
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 optimize a piece of code that was causing performance bottlenecks.
- 02
Tell me about a time you disagreed with a technical decision made by your lead. How did you handle it?
- 03
Describe a project where you had to pivot quickly due to changing requirements.
- 04
How do you prioritize your tasks when you have multiple competing deadlines?
How difficult are the technical interviews?
The technical interviews are challenging but fair. They are designed to test your core engineering problem-solving skills rather than obscure trivia, so focus on foundational knowledge.
Manex AI Software Engineer candidate reports ↗What is the company culture like?
Manex AI fosters a culture of ownership and intellectual curiosity. We value engineers who are proactive, communicate clearly, and take pride in the quality of their work.
Manex AI Software Engineer candidate reports ↗How long does the entire process take?
While timelines vary by candidate and role, we aim to move through the process as efficiently as possible. Expect a clear timeline to be communicated to you during your initial screening.
Manex AI Software Engineer candidate reports ↗Is there a preference for specific technologies?
While we use a specific stack, we prioritize candidates with strong computer science fundamentals who can adapt to our tools quickly. Don't worry if you haven't used every single tool we use in production.
Manex AI Software Engineer candidate reports ↗What topics does Manex AI test in interviews?
Manex AI interviews most often cover Version Control (Git), AI Engineering, Problem Solving, AI Software Engineering, and MLOps. The exact emphasis depends on the specific role you apply for.
Manex 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.
- 01Manex 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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