Axs Software Engineer Interview Guide

The 60-second brief

As a Software Engineer at Axs, you play a vital role in connecting millions of fans with the artists, teams, and live entertainment experiences they love. Operating at the intersection of consumer-facing applications, high-performance global ticketing platforms, and innovative features like applied AI, your work directly powers some of the most iconic venues and events in the world, including massive engagements like the LA28 Olympic and Paralympic Games. You will build and scale reliable, high-demand web applications and backend services that must perform flawlessly under extreme, bursty traffic conditions. This position demands a rare blend of technical precision, architectural foresight, and product thinking.

This guide is scoped to a Software Engineer candidate at Axs.

Focus areas

• LLMs (Large Language Models)
• Full-Stack Development
• Backend Services

The Axs interview process

Axs candidates report 3 rounds over 3-5 weeks. The stages below are what candidates describe, not a published process.

Recruiter/Manager Screen

Initial conversation with a recruiter or engineering manager to discuss your background, alignment, and technical interests.

What to demonstrate

• Initial conversation with a recruiter or engineering manager to discuss your background, alignment, and technical interests
• Depth in LLMs (Large Language Models)

How to prepare

• Be able to walk your CV end to end in two minutes, and say why this company specifically.
• Have your salary expectations, notice period and location constraints ready, and ask for the rest of the loop in writing.

Technical Evaluation

In-depth discussions on architecture, past projects, and practical coding scenarios with senior engineering leaders.

What to demonstrate

• In-depth discussions on architecture, past projects, and practical coding scenarios with senior engineering leaders
• Depth in LLMs (Large Language Models)

How to prepare

• Answer aloud and timed: What strategies do you use to evaluate system performance, reliability, and cost efficiency under heavy load?
• Answer aloud and timed: How do you manage API-first and event-driven patterns in complex enterprise architectures?

On-site Alignment

Final stage involving alignment on cultural fit and hands-on engineering execution.

What to demonstrate

• Final stage involving alignment on cultural fit and hands-on engineering execution
• Depth in LLMs (Large Language Models)

How to prepare

• Answer aloud and timed: Walk through how you would implement a clean, maintainable backend service or API from scratch.
• Answer aloud and timed: How do you approach debugging and troubleshooting unexpected performance bottlenecks in production code?

Where candidates lose points

Focus on real-world scale

When discussing past projects, emphasize how your systems handled high traffic, concurrency bottlenecks, or unexpected failures. Axs builds platforms for massive global events, so operational resilience is always top of mind for interviewers.

Practice collaborative problem-solving

Treat technical screens as a dialogue. If you get stuck or need clarification on a constraint, voice your assumptions and talk through your troubleshooting steps with the interviewer.

Be ready for surprises

As reported in interview experiences, format variations can happen—such as a conversation unexpectedly pivoting into a live coding session. Stay calm, rely on your fundamentals, and focus on writing clean, readable code.

Connect your work to the user experience

Always ground your architectural decisions in the end-user or client value they create, keeping in mind the mission of connecting fans to live entertainment.

Questions & practice

Can you explain how you ensure comprehensive test coverage and robust error handling in your applications?

Medium · Coding and Implementation

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?

Canonicalise a request body into a stable idempotency fingerprint

Medium · parsing · canonicalisation · hashing

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

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

Archive a resource graph without breaking live references or recursing

Medium · graph traversal · topological order · tenant isolation

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

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

Keep soft-deleted accounts from blocking re-registration

Easy · soft delete · unique constraints · engine differences

app_user holds user_id, tenant_id, email CITEXT, password_hash (NULL for SSO principals), email_verified_at, auth_version, status ('invited','active','suspended','deactivated'), created_at, updated_at, deleted_at. Two live accounts for one address inside a tenant must be impossible, but an address freed by a soft delete must be reusable, and the same tenant may delete and re-register it repeatedly. Write the uniqueness DDL for PostgreSQL 16, then the equivalent for MySQL 8 where partial indexes do not exist, and say what each permits once three deleted rows already hold that address.

Approach

1. Start from what is actually unique: not (tenant_id, email), but (tenant_id, email) among live rows. PostgreSQL says that directly — CREATE UNIQUE INDEX app_user_live_email ON app_user (tenant_id, email) WHERE deleted_at IS NULL. A full constraint over the same two columns burns the address permanently the first time someone deletes an account.
2. Keep case-insensitivity in the type or the index, never in the application: CITEXT as given, or UNIQUE (tenant_id, lower(email)) as an expression index where the extension is unavailable. A case-sensitive unique column is exactly how two accounts for one human appear.
3. For MySQL 8 the predicate has to move inside the key: add a discriminator column that is a constant 0 while the row is live and is set to user_id on delete, with UNIQUE (tenant_id, email, deleted_marker). Live rows share the constant and still collide; deleted rows differ from each other and stop colliding.
4. State the NULL variant and its dependency: leaving the marker NULL for deleted rows also works, because a unique index treats NULLs as distinct — true in MySQL, and true in PostgreSQL only under the default NULLS DISTINCT, which PostgreSQL 15 lets you reverse. Check the polarity against the three existing deleted rows: constant-on-live is what preserves the collision you want, and reversing it silently admits duplicate live accounts.

Follow-up

• A deleted account re-registers with the same address the next day. Do the old resource rows follow the new user_id, and how does the API keep the two principals apart?
• How do you honour an erasure request while resource_revision.actor_user_id still references this table?

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?

Can you describe an approach for architecting distributed systems that handle unpredictable, high-concurrency

Medium · Technical and Distributed Systems

Can you describe an approach for architecting distributed systems that handle unpredictable, high-concurrency 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 would you design and optimize data pipelines for AI workflows or large-scale data integrations?

Medium · Technical and Distributed Systems

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?

What strategies do you use to evaluate system performance, reliability, and cost efficiency under heavy load?

Medium · Technical and Distributed Systems

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 manage API-first and event-driven patterns in complex enterprise architectures?

Medium · Technical and Distributed Systems

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?

Walk through how you would implement a clean, maintainable backend service or API from scratch.

Medium · Coding and Implementation

Approach

1. Say who the caller is and what they do when the call fails halfway.
2. Define the identity of a request so a retry cannot double-apply it.
3. Separate accepted, pending, failed and confirmed; they are different facts.
4. 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 do you approach debugging and troubleshooting unexpected performance bottlenecks in production code?

Medium · Coding and Implementation

Approach

1. Establish what changed and when, before forming any theory.
2. Pick a bisection that eliminates candidates whichever way it turns out.
3. Check the instrumentation before believing the symptom.
4. 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?

Suggested practice checklist

Built from the rounds and topics Axs candidates report.

Day 1 — Map the Axs loop

• Write out the reported sequence: Recruiter/Manager Screen, Technical Evaluation, On-site Alignment.
• 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.

Day 2 — Work LLMs (Large Language Models)

• Spend the session on LLMs (Large Language Models), which Axs candidates report being tested on.
• Write one worked example in LLMs (Large Language Models) and time yourself on it.

Deliverable: One timed worked example in LLMs (Large Language Models).

Day 3 — Work Full-Stack Development

• Spend the session on Full-Stack Development, which Axs candidates report being tested on.
• Write one worked example in Full-Stack Development and time yourself on it.

Deliverable: One timed worked example in Full-Stack Development.

Day 4 — Work Backend Services

• Spend the session on Backend Services, which Axs candidates report being tested on.
• Write one worked example in Backend Services and time yourself on it.

Deliverable: One timed worked example in Backend Services.

Day 5 — Answer out loud: Technical and Distributed Systems

• Answer aloud, timed: Can you describe an approach for architecting distributed systems that handle unpredictable, high-concurrency traffic spikes?
• Answer aloud, timed: How would you design and optimize data pipelines for AI workflows or large-scale data integrations?

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

Day 6 — Answer out loud: Coding and Implementation

• Answer aloud, timed: Walk through how you would implement a clean, maintainable backend service or API from scratch.
• Answer aloud, timed: How do you approach debugging and troubleshooting unexpected performance bottlenecks in production code?

Deliverable: Spoken answers to 2 reported Coding and Implementation question(s), under time.

Day 7 — Answer out loud: Behavioral and Process Discussion

• Answer aloud, timed: Tell me about a time you had to collaborate across cross-functional teams to ship a critical feature under a tight deadline.
• Answer aloud, timed: Can you describe a project where you took end-to-end ownership from concept to production?

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

Behavioural preparation

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

Tell me about a time you had to collaborate across cross-functional teams to ship a critical feature under a t

Medium · Behavioral and Process Discussion

Tell me about a time you had to collaborate across cross-functional teams to ship a critical feature under a tight deadline.

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?

Can you describe a project where you took end-to-end ownership from concept to production?

Medium · Behavioral and Process Discussion

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 unexpected changes in product requirements or technical roadblocks during development?

Medium · Behavioral and Process Discussion

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?