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Austin Staffing Software Engineer Interview Guide

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

The 60-second brief

As a Software Engineer at Austin Staffing, you operate at the intersection of high-stakes technical innovation and large-scale industrial execution. You are not merely writing code; you are building the foundational software architectures that power complex systems, ranging from autonomous vehicle sensor stacks to global cloud logistics infrastructure. Your work directly influences the reliability, safety, and efficiency of systems that operate in real-world, mission-critical environments. This role is designed for engineers who thrive on complexity and possess a deep desire to solve "impossible" problems. Whether you are optimizing low-latency C++ code for embedded systems or architecting data-driven financial models for supply chains, your output serves as the backbone for our clients' most ambitious technical programs.

This guide is scoped to a Software Engineer candidate at Austin Staffing.

Focus areas

  • C++
  • Embedded Linux / POSIX
  • Runtime software for autonomy integration

The Austin Staffing interview process

Austin Staffing candidates report 6 rounds over 4-6 weeks. The stages below are what candidates describe, not a published process.

Initial Screening

The process begins with an initial screening to evaluate your fit for the role.

What to demonstrate

  • The process begins with an initial screening to evaluate your fit for the role
  • Depth in C++

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 Screenings

You will undergo deep-dive technical screenings to assess your technical capabilities.

What to demonstrate

  • You will undergo deep-dive technical screenings to assess your technical capabilities
  • Depth in C++

How to prepare

  • Answer aloud and timed: How do you approach debugging a race condition in a multi-threaded system?
  • Answer aloud and timed: What is your process for performing a functional decomposition of a complex system requirement?

Project Experience Discussion

Broader discussions regarding your past project experiences and design philosophy will take place.

What to demonstrate

  • Broader discussions regarding your past project experiences and design philosophy will take place
  • Depth in C++

How to prepare

  • Answer aloud and timed: Describe your experience with embedded build systems like Yocto or Buildroot.
  • Answer aloud and timed: How would you architect an interface contract between a high-level autonomy stack and low-level hardware?

Real-World Scenarios

You will be presented with scenarios that mirror actual challenges faced by the teams.

What to demonstrate

  • You will be presented with scenarios that mirror actual challenges faced by the teams
  • Depth in C++

How to prepare

  • Answer aloud and timed: Describe a time you had to optimize a system to reduce operational expenses or improve throughput.
  • Answer aloud and timed: How do you design for "graceful degradation" when a critical component of a system fails?

Behavioral Assessments

Prepare for behavioral assessments to evaluate how you handle ambiguity and integrate into a team.

What to demonstrate

  • Prepare for behavioral assessments to evaluate how you handle ambiguity and integrate into a team
  • Depth in C++

How to prepare

  • Prepare three examples from your own work, each with a decision you made and an outcome you can quantify.
  • Re-read the description of the behavioral assessments above and write down what you would ask to confirm before it.

Final Offer Discussion

The process concludes with a discussion regarding the final offer.

What to demonstrate

  • The process concludes with a discussion regarding the final offer
  • Depth in C++

How to prepare

  • Answer aloud and timed: How do you balance the need for high-quality code with the pressure of tight project deadlines?
  • Answer aloud and timed: Describe a situation where you had to mentor a junior developer. What was your approach?

Where candidates lose points

Show your work

When solving technical problems, talk through your thought process aloud. We are as interested in how you approach a problem as we are in the final solution.

Ask clarifying questions

Don't rush to code. Take a moment to clarify requirements and identify potential edge cases before starting.

Prepare for behavioral questions

Use the STAR method to ensure your answers are structured and highlight your specific impact.

Review your resume

Be prepared to discuss every project listed on your resume in detail, including your specific role, the technical challenges you faced, and the outcomes.

Stay updated

Familiarize yourself with the latest developments in the specific domain you are applying for, such as advancements in ADAS sensors or modern supply chain logistics.

Questions & practice

How do you manage memory and ensure low-latency performance in C++ within a Linux-based environment?

Medium · Technical & Domain Expertise

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?

How do you approach debugging a race condition in a multi-threaded system?

Medium · Technical & Domain Expertise

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?

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?

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?

Stop tag and share joins from fanning out a page

Medium · join fan-out · semi-joins · many-to-many

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

  1. 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.
  2. 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.
  3. 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.
  4. 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 the trade-offs between using TCP/IP versus UDP when interfacing with real-time sensor data.

Medium · Technical & Domain Expertise

Approach

  1. Clarify what is being asked and what a complete answer contains.
  2. State your assumptions explicitly before working the problem.
  3. Say what you would check first and why it is the highest-information step.
  4. Work from the requirement backwards to the design.

Follow-up

  • What assumption would you test first?
  • How would you know your answer was wrong?

What is your process for performing a functional decomposition of a complex system requirement?

Medium · Technical & Domain Expertise

Approach

  1. Clarify what is being asked and what a complete answer contains.
  2. State your assumptions explicitly before working the problem.
  3. Say what you would check first and why it is the highest-information step.
  4. Work from the requirement backwards to the design.

Follow-up

  • What assumption would you test first?
  • How would you know your answer was wrong?

How would you architect an interface contract between a high-level autonomy stack and low-level hardware?

Medium · System Design & Scalability

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 design for "graceful degradation" when a critical component of a system fails?

Medium · System Design & Scalability

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 ensure your code remains scalable as the project moves from prototype to production?

Medium · System Design & Scalability

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?

One log partition stops advancing while the others drain

Easy · poison message · head-of-line blocking · consumer lag

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

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

Suggested practice checklist

Built from the rounds and topics Austin Staffing candidates report.

Day 1 — Map the Austin Staffing loop

  • Write out the reported sequence: Initial Screening, Technical Screenings, Project Experience Discussion, Real-World Scenarios, Behavioral Assessments, Final Offer Discussion.
  • 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 6 reported rounds, with the weakest marked.

Day 2 — Work C++

  • Spend the session on C++, which Austin Staffing candidates report being tested on.
  • Write one worked example in C++ and time yourself on it.

Deliverable: One timed worked example in C++.

Day 3 — Work Embedded Linux / POSIX

  • Spend the session on Embedded Linux / POSIX, which Austin Staffing candidates report being tested on.
  • Write one worked example in Embedded Linux / POSIX and time yourself on it.

Deliverable: One timed worked example in Embedded Linux / POSIX.

Day 4 — Work Runtime software for autonomy integration

  • Spend the session on Runtime software for autonomy integration, which Austin Staffing candidates report being tested on.
  • Write one worked example in Runtime software for autonomy integration and time yourself on it.

Deliverable: One timed worked example in Runtime software for autonomy integration.

Day 5 — Answer out loud: Technical & Domain Expertise

  • Answer aloud, timed: How do you manage memory and ensure low-latency performance in C++ within a Linux-based environment?
  • Answer aloud, timed: Explain the trade-offs between using TCP/IP versus UDP when interfacing with real-time sensor data.

Deliverable: Spoken answers to 2 reported Technical & Domain Expertise question(s), under time.

Day 6 — Answer out loud: System Design & Scalability

  • Answer aloud, timed: How would you architect an interface contract between a high-level autonomy stack and low-level hardware?
  • Answer aloud, timed: Describe a time you had to optimize a system to reduce operational expenses or improve throughput.

Deliverable: Spoken answers to 2 reported System Design & Scalability question(s), under time.

Day 7 — Answer out loud: Behavioral & Leadership

  • Answer aloud, timed: Tell me about a time you had to resolve a technical disagreement with a colleague. How did you reach a consensus?
  • Answer aloud, timed: How do you balance the need for high-quality code with the pressure of tight project deadlines?

Deliverable: Spoken answers to 2 reported Behavioral & Leadership question(s), under time.

Behavioural preparation

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

Describe your experience with embedded build systems like Yocto or Buildroot.

Medium · Technical & Domain Expertise

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?

Describe a time you had to optimize a system to reduce operational expenses or improve throughput.

Medium · System Design & Scalability

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 time you had to resolve a technical disagreement with a colleague. How did you reach a consens

Medium · Behavioral & Leadership

Tell me about a time you had to resolve a technical disagreement with a colleague. How did you reach a consensus?

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 balance the need for high-quality code with the pressure of tight project deadlines?

Medium · Behavioral & Leadership

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?

Describe a situation where you had to mentor a junior developer. What was your approach?

Medium · Behavioral & Leadership

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 project you led from concept through implementation.

Medium · Behavioral & Leadership

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?