cswork / libraryOpen practice workspace

Interview guides · Zephyr AI

Zephyr AI Software Engineer Interview Guide

Zephyr AI Software Engineer interview guide: reported rounds, questions and how candidates describe the process.

The 60-second brief

At Zephyr AI, the Software Engineer role is central to building and scaling the advanced technical platforms that power our data-driven products. Engineers here work on high-impact systems that process complex datasets, optimize computational workflows, and deliver robust software solutions. Whether you are developing scalable data pipelines, optimizing system architecture, or integrating specialized domain logic, your work directly influences the speed, reliability, and precision of our product offerings. This position demands a unique blend of core software engineering expertise and structured problem-solving. Because our engineering challenges span both massive data scale and intricate technical configurations, you will collaborate closely with cross-functional teams of scientists, product managers, and infrastructure specialists.

This guide is scoped to a Software Engineer candidate at Zephyr AI.

Focus areas

  • Software Engineering (General)
  • Technical Interviewing
  • Communication Skills

The Zephyr AI interview process

Zephyr AI candidates report 2 rounds over 2-4 weeks. The stages below are what candidates describe, not a published process.

Telephonic Technical Screening

Initial screening focused on core programming concepts and basic problem-solving to assess foundational skills.

What to demonstrate

  • Initial screening focused on core programming concepts and basic problem-solving to assess foundational skills
  • Depth in Software Engineering (General)

How to prepare

  • Answer aloud and timed: How would you design a distributed data pipeline to process and aggregate high-volume real-time events?
  • Answer aloud and timed: Describe how you would implement a fault-tolerant caching strategy for a service experiencing highly variable traffic spikes.

Onsite Interview Loop

Three consecutive face-to-face technical interviews covering system design, advanced coding, and domain-specific challenges.

What to demonstrate

  • Three consecutive face-to-face technical interviews covering system design, advanced coding, and domain-specific challenges
  • Depth in Software Engineering (General)

How to prepare

  • Answer aloud and timed: How do you approach database schema design and indexing when optimizing for complex, read-heavy queries?
  • Answer aloud and timed: Design an API gateway that handles rate limiting, authentication, and graceful degradation for downstream microservices.

Where candidates lose points

Clarify Ambiguity Early

When presented with a system design or coding problem, never jump straight into writing code. Ask clarifying questions to establish scope, scale, constraints, and functional requirements first.

Think Out Loud

Your interviewers want to understand your thought process, not just see a working solution. Maintain a continuous dialogue, explaining your approach, the trade-offs you are considering, and why you are choosing one path over another.

Follow Up Proactively

If you experience delays or lack of communication after your technical rounds, do not hesitate to reach out to the recruiting team or hiring manager to request an update on your status.

Test Your Code Thoroughly

Once you complete a coding solution, walk through it with sample inputs and edge cases. Proactively identify potential bugs, null pointers, or performance bottlenecks before declaring your code complete.

Questions & practice

Given a massive dataset, how would you write an memory-efficient algorithm to identify and remove duplicate re

Medium · Algorithmic Problem Solving

Given a massive dataset, how would you write an memory-efficient algorithm to identify and remove duplicate records?

Approach

  1. Say what the runtime actually does before reasoning about the code.
  2. Name what is shared across threads and what owns each piece of state.
  3. Identify the window where an invariant is briefly untrue.
  4. Distinguish a value from a reference to it, and say which one you handed out.

Follow-up

  • What happens if two callers reach this at the same time?
  • Where could this allocate more than you expect?

Explain how you would optimize a search algorithm operating on a highly connected graph structure.

Medium · Algorithmic Problem Solving

Approach

  1. Restate the input: its shape, its size, and what is guaranteed about it.
  2. Name the brute-force solution and its complexity before improving on it.
  3. Choose the data structure from the access pattern, not from familiarity.
  4. 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 do you implement a custom queue structure that supports concurrent read and write operations safely?

Medium · Algorithmic Problem Solving

Approach

  1. Say what the runtime actually does before reasoning about the code.
  2. Name what is shared across threads and what owns each piece of state.
  3. Identify the window where an invariant is briefly untrue.
  4. 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?

Write a function to parse and validate complex nested configurations, ensuring optimal time and space complexi

Medium · Algorithmic Problem Solving

Write a function to parse and validate complex nested configurations, ensuring optimal time and space complexity.

Approach

  1. Restate the input: its shape, its size, and what is guaranteed about it.
  2. Name the brute-force solution and its complexity before improving on it.
  3. Choose the data structure from the access pattern, not from familiarity.
  4. 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 why the owner filter ignores the listing index

Medium · composite index · query plans · sort elimination

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

  1. 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.
  2. 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.
  3. 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.
  4. 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?

Write the update path that detects a concurrent edit

Medium · optimistic concurrency · transactional outbox · audit history

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

  1. 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.
  2. 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.
  3. 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.
  4. 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?

How would you design a distributed data pipeline to process and aggregate high-volume real-time events?

Medium · Systems Architecture and Design

Approach

  1. Fix the scope first: who calls this, how often, and what they do when it fails.
  2. Name the read and write paths separately; they rarely have the same bottleneck.
  3. Choose a partition key and say what query it makes expensive.
  4. State the consistency you need, and where you are willing to be stale.

Follow-up

  • What breaks first when traffic grows ten times?
  • How does this behave when that dependency is down for an hour?

Describe how you would implement a fault-tolerant caching strategy for a service experiencing highly variable

Medium · Systems Architecture and Design

Describe how you would implement a fault-tolerant caching strategy for a service experiencing highly variable traffic spikes.

Approach

  1. Fix the scope first: who calls this, how often, and what they do when it fails.
  2. Name the read and write paths separately; they rarely have the same bottleneck.
  3. Choose a partition key and say what query it makes expensive.
  4. 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 database schema design and indexing when optimizing for complex, read-heavy queries?

Medium · Systems Architecture and Design

Approach

  1. Fix the scope first: who calls this, how often, and what they do when it fails.
  2. Name the read and write paths separately; they rarely have the same bottleneck.
  3. Choose a partition key and say what query it makes expensive.
  4. 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?

Design an API gateway that handles rate limiting, authentication, and graceful degradation for downstream micr

Medium · Systems Architecture and Design

Design an API gateway that handles rate limiting, authentication, and graceful degradation for downstream microservices.

Approach

  1. Fix the scope first: who calls this, how often, and what they do when it fails.
  2. Name the read and write paths separately; they rarely have the same bottleneck.
  3. Choose a partition key and say what query it makes expensive.
  4. 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?

Listing latency scales with page size, not with filters

Easy · n+1 · latency · query batching

The tenant listing endpoint reads resource filtered by tenant_id and status, ordered by updated_at DESC, and returns each row plus the owner's display name from app_user and the actor of that resource's latest resource_revision. p99 is 55 ms at 10 rows per page and 1.4 s at 200. Database telemetry shows 401 statements per request, each under 1 ms, and nothing in the slow-query log. Diagnose the cause and give the fix, stating the statement count per request and the p99 you expect afterwards.

Approach

  1. Read the counters before forming a theory. 401 statements for 200 rows is one driver query plus two per row, and sub-millisecond execution with an empty slow-query log rules out a bad plan. The time is round trips, which is why it is invisible in every per-query metric and scales with rows returned rather than with filter selectivity.
  2. Name the two per-row statements from their normalised text: a single-row app_user lookup by user_id, and a resource_revision lookup by resource_id ordered by version DESC LIMIT 1. Confirm by dropping those two response fields and watching the statement count fall to one. That locates the calls in the serialisation layer, not the repository.
  3. Check that the arithmetic accounts for the whole gap. Measure one round trip to the replica in isolation; 400 trips at roughly 3 ms of network plus 0.2 ms of execution is about 1.3 s on top of a 55 ms baseline, which matches. If the multiplication had fallen short, the N+1 would only be part of the story and you would keep looking.
  4. Batch both lookups. Collect owner_user_ids and resource_ids from the driver query, then issue WHERE tenant_id = $1 AND user_id = ANY($2) for the users, and PostgreSQL's SELECT DISTINCT ON (resource_id) ... WHERE resource_id = ANY($2) ORDER BY resource_id, version DESC for the latest revision, which the UNIQUE (resource_id, version) index serves directly. On an engine without DISTINCT ON, use a lateral join or a row_number window. Three statements per request at any page size.

Follow-up

  • The page size is capped at 200 today. What breaks first if it is raised to 2,000, and is it still this bug?
  • How do you stop the next N+1 from reaching production, given that no individual query is slow and the endpoint's tests pass?

Suggested practice checklist

Built from the rounds and topics Zephyr AI candidates report.

Day 1 — Map the Zephyr AI loop

  • Write out the reported sequence: Telephonic Technical Screening, Onsite Interview Loop.
  • 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.

Day 2 — Work Software Engineering (General)

  • Spend the session on Software Engineering (General), which Zephyr 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).

Day 3 — Work Technical Interviewing

  • Spend the session on Technical Interviewing, which Zephyr AI candidates report being tested on.
  • Write one worked example in Technical Interviewing and time yourself on it.

Deliverable: One timed worked example in Technical Interviewing.

Day 4 — Work Communication Skills

  • Spend the session on Communication Skills, which Zephyr AI candidates report being tested on.
  • Write one worked example in Communication Skills and time yourself on it.

Deliverable: One timed worked example in Communication Skills.

Day 5 — Answer out loud: Systems Architecture and Design

  • Answer aloud, timed: How would you design a distributed data pipeline to process and aggregate high-volume real-time events?
  • Answer aloud, timed: Describe how you would implement a fault-tolerant caching strategy for a service experiencing highly variable traffic spikes.

Deliverable: Spoken answers to 2 reported Systems Architecture and Design question(s), under time.

Day 6 — Answer out loud: Algorithmic Problem Solving

  • Answer aloud, timed: Given a massive dataset, how would you write an memory-efficient algorithm to identify and remove duplicate records?
  • Answer aloud, timed: Explain how you would optimize a search algorithm operating on a highly connected graph structure.

Deliverable: Spoken answers to 2 reported Algorithmic Problem Solving question(s), under time.

Day 7 — Answer out loud: Behavioral and Professional Collaboration

  • Answer aloud, timed: Describe a time when you had to make a significant technical trade-off under a tight deadline. How did you decide?
  • Answer aloud, timed: How do you handle a situation where you disagree with a peer or manager on a critical architectural decision?

Deliverable: Spoken answers to 2 reported Behavioral and Professional Collaboration question(s), under time.

Behavioural preparation

Behavioural rounds judge the decision you made and what it cost.

Describe a time when you had to make a significant technical trade-off under a tight deadline. How did you dec

Medium · Behavioral and Professional Collaboratio

Describe a time when you had to make a significant technical trade-off under a tight deadline. How did you decide?

Approach

  1. Pick a story where you made the decision, not one where you watched it.
  2. State the situation in two sentences and spend the rest on the reasoning.
  3. Give the blast radius: what could have broken, and what you measured.
  4. 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 a situation where you disagree with a peer or manager on a critical architectural decision?

Medium · Behavioral and Professional Collaboratio

Approach

  1. Pick a story where you made the decision, not one where you watched it.
  2. State the situation in two sentences and spend the rest on the reasoning.
  3. Give the blast radius: what could have broken, and what you measured.
  4. 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 complex technical bug you encountered in production. How did you diagnose, resolve, and preven

Medium · Behavioral and Professional Collaboratio

Tell me about a complex technical bug you encountered in production. How did you diagnose, resolve, and prevent it from recurring?

Approach

  1. Pick a story where you made the decision, not one where you watched it.
  2. State the situation in two sentences and spend the rest on the reasoning.
  3. Give the blast radius: what could have broken, and what you measured.
  4. 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 tasks and maintain high code quality when requirements are rapidly changing?

Medium · Behavioral and Professional Collaboratio

Approach

  1. Pick a story where you made the decision, not one where you watched it.
  2. State the situation in two sentences and spend the rest on the reasoning.
  3. Give the blast radius: what could have broken, and what you measured.
  4. 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?