A Software Engineer at Level AI plays a pivotal role in shaping the future of customer experience through advanced artificial intelligence. You will be responsible for building, scaling, and maintaining the infrastructure that powers our real-time intelligence platform, which transforms unstructured customer interactions into actionable insights. Your work directly impacts how enterprises understand their customers, requiring a balance of robust engineering practices and innovative problem-solving. This role is both critical and intellectually demanding. You will navigate complex technical challenges, from optimizing data processing pipelines to designing scalable system architectures. Whether you are working on core product features or backend infrastructure, you are expected to operate with high autonomy and a deep commitment to code quality, efficiency, and system reliability. ##### Tip Candidates should demonstrate a proactive approach to engineering. At Level AI, we prioritize engineers who don't just write code, but who actively seek to understand the business problems their software is solving.
Technical Deep Dives
reportedA series of technical interviews focusing on foundational coding proficiency.
What to demonstrate
- A series of technical interviews focusing on foundational coding proficiency
- Depth in Data Structures & Algorithms (DSA)
How to prepare
- Answer aloud and timed: Solve a medium-level dynamic programming problem.
- Answer aloud and timed: Explain the time and space complexity of your proposed approach.
Data Structures and Algorithms
reportedTechnical rounds that assess your knowledge and skills in DSA.
What to demonstrate
- Technical rounds that assess your knowledge and skills in DSA
- Depth in Data Structures & Algorithms (DSA)
How to prepare
- Answer aloud and timed: Optimize an existing algorithm to handle larger datasets.
- Answer aloud and timed: Implement a solution for a common array or string manipulation problem.
System Design
reportedInterviews that evaluate your ability to design complex systems.
What to demonstrate
- Interviews that evaluate your ability to design complex systems
- Depth in Data Structures & Algorithms (DSA)
How to prepare
- Answer aloud and timed: Design a scalable component for a real-time analytics platform.
- Answer aloud and timed: Discuss the trade-offs between different database choices for our specific use case.
Discussions with Management
reportedConversations with engineering management and potentially leadership.
What to demonstrate
- Conversations with engineering management and potentially leadership
- Depth in Data Structures & Algorithms (DSA)
How to prepare
- Answer aloud and timed: How would you handle high-concurrency requests in our system?
- Answer aloud and timed: Explain the architecture of a past project and justify your design decisions.
PracHub editorial advice for the preparation topics above.
Talk through your process
Silence is your enemy during coding interviews. Even if you are stuck, explain what you are thinking and what potential paths you are exploring.
Ask clarifying questions
Never start coding until you are 100% sure you understand the constraints and expectations.
Refine your resume projects
Be prepared to dive deep into any project you list on your resume. You should know the architecture, the challenges you faced, and the specific impact of your work.
Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.
Solve a medium-level dynamic programming problem.
Solve a medium-level dynamic programming problem.
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?
Explain the time and space complexity of your proposed approach.
Explain the time and space complexity of your proposed approach.
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?
Optimize an existing algorithm to handle larger datasets.
Optimize an existing algorithm to handle larger datasets.
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 solution for a common array or string manipulation problem.
Implement a solution for a common array or string manipulation problem.
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 handle high-concurrency requests in our system?
How would you handle high-concurrency requests in our system?
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?
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?
Explain why the owner filter ignores the listing index
The only index on resource is (tenant_id, status, updated_at DESC, resource_id DESC). A new endpoint returns one user's resources across all statuses, newest created first: WHERE tenant_id = $1 AND owner_user_id = $2 ORDER BY created_at DESC LIMIT 20. On a tenant with 2M rows it takes 900 ms and EXPLAIN shows a sort above a large scan. Explain precisely why the existing index cannot serve it, give the index that can, and state which of these the new index still will not help: owner_user_id alone across tenants; the same query ordered by updated_at. PostgreSQL 16.
Approach
- Separate the two jobs an index does. For filtering, a composite btree is seekable only on a left prefix, so with no predicate on status the scan can at best range over tenant_id and test owner_user_id per row; PostgreSQL 16 has no btree skip scan to jump the unconstrained column.
- For ordering, the index is sorted by (status, updated_at) within a tenant and not by created_at, so the LIMIT cannot stop early: every matching row is read and then sorted. That is the 'Sort Method: top-N heapsort' line, and it is why the plan reads 2M rows to answer with 20.
- Derive the replacement from the access path — equality, equality, then the ordering column: CREATE INDEX CONCURRENTLY ON resource (tenant_id, owner_user_id, created_at DESC). The scan seeks to the (tenant, owner) range and walks 20 entries in order, so the Sort node disappears along with the row-read.
- Treat INCLUDE (title, status) as conditional, not free. An index-only scan still visits the heap for any row whose page is not marked all-visible, so on a table taking 1.2k writes/second the win depends on autovacuum keeping the visibility map current, and the wider index costs more on every insert.
Follow-up
- 90% of rows are status='active'. Would a partial index WHERE status = 'active' change your answer, and for which of the three queries?
- A dashboard runs this for 40 owners in one page load. What changes about the design?
Design a scalable component for a real-time analytics platform.
Design a scalable component for a real-time analytics platform.
Approach
- Fix the scope first: who calls this, how often, and what they do when it fails.
- Name the read and write paths separately; they rarely have the same bottleneck.
- Choose a partition key and say what query it makes expensive.
- State the consistency you need, and where you are willing to be stale.
Follow-up
- What breaks first when traffic grows ten times?
- How does this behave when that dependency is down for an hour?
Discuss the trade-offs between different database choices for our specific use case.
Discuss the trade-offs between different database choices for our specific use case.
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 architecture of a past project and justify your design decisions.
Explain the architecture of a past project and justify your design decisions.
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?
One log partition stops advancing while the others drain
Search results for a subset of tenants are hours stale; the rest are current. The projection consumer reports lag of zero on 15 of 16 partitions and 400,000 on one. Its error rate is flat and its CPU is idle. outbox_event has no pending rows older than a second, so the relay has published everything it holds. Identify the mechanism, give the ordered checks, and state what you do in the first ten minutes versus what you change permanently.
Approach
- Read the lag distribution first. A slow consumer lags everywhere; zero on fifteen partitions and 400,000 on one is not throughput. Idle CPU on the stuck partition means the consumer is not advancing its offset at all, which points at one message it cannot get past rather than at a rate problem.
- Exonerate the producer before touching the consumer. No pending outbox rows older than a second means the relay published, so the event exists in the log. This separates never sent from sent and never applied, which are different code paths and usually different owners.
- Read the message at the stuck offset and the handler's log lines for its event_id. A flat error rate with no progress has two explanations and you must distinguish them: the handler is throwing and the retry loop is swallowing it, or the handler is blocking on something and never returning. Idle CPU with no error lines favours the second.
- Mitigate before diagnosing further. Move the offending event to a dead-letter store and commit the offset past it. Adding consumers does nothing here, because a partition is consumed by exactly one member of the group, and the blast radius is every aggregate hashed to that partition, not only the aggregate that produced the bad event.
Follow-up
- The dead-lettered event carried aggregate_version 7 and the projection had applied 6. What must the replay do differently if 8 and 9 landed in the meantime?
- How do you show staleness to the user while the partition is behind, given the API already returns the projection's watermark?
Built from the rounds and topics Level AI candidates report.
Prepare, practise & reflect
One practical outcome each day. Spend longer where you need it.
0 / 7 done01Map the Level AI loop
- Write out the reported sequence: Technical Deep Dives, Data Structures and Algorithms, System Design, Discussions with Management.
- 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 4 reported rounds, with the weakest marked.
02Work Data Structures & Algorithms (DSA)
- Spend the session on Data Structures & Algorithms (DSA), which Level AI 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 Dynamic Programming (DP)
- Spend the session on Dynamic Programming (DP), which Level AI candidates report being tested on.
- Write one worked example in Dynamic Programming (DP) and time yourself on it.
Deliverable: One timed worked example in Dynamic Programming (DP).
04Work System Design
- Spend the session on System Design, which Level AI candidates report being tested on.
- Write one worked example in System Design and time yourself on it.
Deliverable: One timed worked example in System Design.
05Answer out loud: Data Structures & Algorithms
- Answer aloud, timed: Solve a medium-level dynamic programming problem.
- Answer aloud, timed: Explain the time and space complexity of your proposed approach.
Deliverable: Spoken answers to 2 reported Data Structures & Algorithms question(s), under time.
06Answer out loud: System Design
- Answer aloud, timed: Design a scalable component for a real-time analytics platform.
- Answer aloud, timed: Discuss the trade-offs between different database choices for our specific use case.
Deliverable: Spoken answers to 2 reported System Design question(s), under time.
07Answer out loud: Behavioral & Leadership
- Answer aloud, timed: Describe a time you had to pivot your technical approach due to changing requirements.
- Answer aloud, timed: How do you handle disagreements with team members regarding architectural decisions?
Deliverable: Spoken answers to 2 reported Behavioral & Leadership question(s), under time.
Expand any day for tasks and deliverables. Your progress is saved on this device.
Behavioural rounds judge the decision you made and what it cost.
Describe a time you had to pivot your technical approach due to changing requirements.
Describe a time you had to pivot your technical approach 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 handle disagreements with team members regarding architectural decisions?
How do you handle disagreements with team members regarding architectural decisions?
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 us about a project where you had to drive a solution from conception to deployment.
Tell us about a project where you had to drive a solution from conception to deployment.
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 pivot your technical approach due to changing requirements.
- 02
How do you handle disagreements with team members regarding architectural decisions?
- 03
Tell us about a project where you had to drive a solution from conception to deployment.
How long should I spend preparing for the coding rounds?
Dedicate at least 3–4 weeks to consistent practice, focusing on medium-to-hard level problems. Ensure you can explain your logic as you code, as this is just as important as the final solution.
Level AI Software Engineer candidate reports ↗What differentiates successful candidates?
Successful candidates don't just solve the problem; they discuss the trade-offs of their approach and consider the system-wide impact of their code. They also communicate clearly and effectively under pressure.
Level AI Software Engineer candidate reports ↗How many rounds are typical?
You should expect 3–4 technical rounds, potentially followed by a leadership or culture-fit interview. The process is rigorous and intended to ensure a strong long-term match.
Level AI Software Engineer candidate reports ↗What topics does Level AI test in interviews?
Level AI interviews most often cover Data Structures & Algorithms (DSA), System Design, Dynamic Programming (DP), Coding Interviews (Implementation), and Opportunity Sourcing (Self-sourcing). The exact emphasis depends on the specific role you apply for.
Level 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.
- 01Level 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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