As a Software Engineer (often designated as an Engineering Scientist or Application Analyst) at the University of Texas at Austin, you will play a vital role in building, maintaining, and scaling software solutions that support world-class academic research, institutional operations, and healthcare services. Your work directly empowers researchers, students, faculty, and clinicians by providing robust digital infrastructure, specialized R&D applications, and enterprise data systems. Whether you are developing custom modeling software, integrating complex healthcare pipelines, or optimizing large-scale data systems, your technical contributions drive innovation across the university. This position sits at the intersection of academic rigor and practical engineering scale. You will collaborate closely with multidisciplinary teams, including principal investigators, academic researchers, system administrators, and product stakeholders, translating complex domain requirements into clean, maintainable software. The environment demands both strong core computer science fundamentals and the flexibility to adapt to diverse research and institutional domains—ranging from high-performance computing and electrical engineering applications to specialized enterprise software like Epic platforms. The role offers a unique balance of intellectually stimulating problem-solving and meaningful public impact.
Phone Screen
reportedInitial screening call to evaluate candidate's background and fit for the role.
What to demonstrate
- Initial screening call to evaluate candidate's background and fit for the role
- Depth in Troubleshooting / debugging
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 Interviews
reportedOne or more interviews that may include coding challenges and technical assessments.
What to demonstrate
- One or more interviews that may include coding challenges and technical assessments
- Depth in Troubleshooting / debugging
How to prepare
- Answer aloud and timed: How would you troubleshoot a ticket you're not familiar with?
- Answer aloud and timed: What does an interrupt look like in a keyboard?
Behavioral Assessment
reportedEvaluation of problem-solving approach and teamwork skills through behavioral questions.
What to demonstrate
- Evaluation of problem-solving approach and teamwork skills through behavioral questions
- Depth in Troubleshooting / debugging
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 assessment above and write down what you would ask to confirm before it.
PracHub editorial advice for the preparation topics above.
Emphasize clarity over cleverness
When walking through coding solutions or pseudo-code, prioritize readable, well-structured logic that your interviewers can easily follow and discuss with you.
Prepare for behavioral narratives
Be ready to share concrete stories from your past work history that highlight your teamwork, troubleshooting tenacity, and ability to handle ambiguous technical requirements.
Understand the institutional context
Research the specific department or lab you are interviewing with; showing an understanding of their domain—whether it's electrical engineering, healthcare analytics, or high-performance computing—will immediately build credibility.
Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.
Swap two values in an array.
Swap two values in an array.
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?
Archive a resource graph without breaking live references or recursing
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
- 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.
- 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.
- 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.
- 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?
Collapse a redelivered event batch into per-aggregate high-water marks
You drain a batch of up to 5,000,000 events, each (aggregate_id BIGINT, aggregate_version INT, event_type, payload). The log guarantees order within one aggregate only; the batch merges 64 partitions, and a relay failover has redelivered a range, so an older version for an aggregate can appear after a newer one. Given a map of last_applied_version per aggregate, produce the events worth applying, at most one per (aggregate_id, version), plus the count discarded. Target O(n) time. State the memory for 2,000,000 distinct aggregates and what you do when it does not fit.
Approach
- One pass, one hash map from aggregate_id to the highest version kept, and a discard counter. An event whose version is at or below last_applied_version for its aggregate is dropped without further work, which is the whole reason the event carries its version rather than a delta. O(n) expected time, O(d) space in distinct aggregates.
- Keep the maximum, never the last occurrence. The redelivered range means the final appearance of an aggregate in the batch can be an older version than one seen earlier in the same batch, so last-wins applies stale state over newer state and the projection regresses with no error anywhere.
- Cost the memory instead of calling it large: an 8-byte key plus a 4-byte version is 12 bytes of payload, and an open-addressed table held at a 0.7 load factor costs roughly 17 bytes per entry before per-slot metadata, so 2,000,000 aggregates is tens of megabytes in a native layout and several times that in a runtime that boxes both key and value.
- If the distinct set exceeds memory, partition on hash(aggregate_id) mod P and reduce each partition independently. Every event for one aggregate hashes to the same partition, so the per-partition result is exact and the merge is concatenation rather than a second reduction.
Follow-up
- The payload is a patch rather than a snapshot, so applying only the highest version loses the intermediate changes. What changes in your reduction?
- How do you detect that version 7 arrived while version 6 was never delivered, and what should the consumer do about the gap?
Hold a per-tenant active cap against concurrent creates
A tenant on the standard plan may hold at most 50 resources with status='active'. The create handler runs SELECT count(*) FROM resource WHERE tenant_id = $1 AND status = 'active', compares to 50, then inserts. Two creates arrive 3 ms apart on different instances and the tenant lands at 51. Name the anomaly, say whether PostgreSQL 16 READ COMMITTED or REPEATABLE READ prevents it and why, then give an implementation that holds the cap at READ COMMITTED with the exact statements. Finally, say what changes when the cap is 'at most one running export per tenant' on job_run.
Approach
- Name it: write skew. The two transactions read an overlapping set and write disjoint rows, so there is no row-level conflict for the engine to detect and each commit is individually legal.
- Rule out the levels precisely. READ COMMITTED takes a fresh snapshot per statement and takes no lock on the counted rows, so both see 49. PostgreSQL's REPEATABLE READ is snapshot isolation: it removes non-repeatable reads and phantoms within the snapshot but still admits write skew, because the anomaly is not a re-read of a changed row, it is a read of a set that a concurrent transaction invalidates. Only SERIALIZABLE closes it, by tracking the read dependency and aborting one transaction with SQLSTATE 40001 — a guarantee that exists only if the application re-runs the whole transaction from the read.
- Convert the set predicate into a single-row conflict: keep tenant.active_resource_count and run UPDATE tenant SET active_resource_count = active_resource_count + 1 WHERE tenant_id = $1 AND active_resource_count < 50 in the same transaction as the INSERT. Zero affected rows is the cap, returned as 409. The row lock serialises the decision at any isolation level, and contention is bounded to one tenant's row — which is also the fair-scheduling unit, unlike a global counter that would convoy every tenant behind one row.
- State the cost you just took on: a counter is a second source of truth that can drift, so every path that changes status must adjust it inside the same transaction, and a periodic reconciliation has to exist, with resource_revision as the authority for what the count should have been.
Follow-up
- A resource moves from archived back to active. Which statements change, and what breaks if the counter update and the status change land in different transactions?
- The cap becomes plan-dependent and a plan can change mid-month. Where does the number 50 live, and who reads it?
Denormalise tenant onto revisions and backfill it live
resource_revision (revision_id, resource_id, version, actor_user_id, change_kind, patch, request_id, created_at) has 400M rows and no tenant column; tenant_id lives only on resource. Two reads need it: a tenant-scoped audit feed ordered by created_at DESC, and an offboarding purge. Both join back to resource today. Justify adding tenant_id to resource_revision against those two reads, name the anomaly the copy introduces and the constraint that prevents it, then give the ordered migration for a live table taking 1.2k writes/second — the lock each step takes, how the backfill is batched, and where each step stops being reversible. PostgreSQL 16.
Approach
- Justify from the access path rather than from taste. Without the column, the audit feed either scans resource_revision by created_at and discards other tenants' rows, or resolves the tenant's resource_ids first and probes with them — both proportional to the tenant's whole history rather than to one page. With (tenant_id, created_at DESC, revision_id DESC) it is a seek that stops at 50 rows, and the purge becomes a ranged delete instead of a join.
- Name the cost exactly: a second copy of a fact can disagree with the first. Make the disagreement unwritable rather than documented — add UNIQUE (resource_id, tenant_id) on resource so it can serve as a foreign-key target, then FOREIGN KEY (resource_id, tenant_id) REFERENCES resource (resource_id, tenant_id) on the revision table. A revision can then only ever carry its parent's tenant.
- Step one, expand: ALTER TABLE resource_revision ADD COLUMN tenant_id BIGINT NULL, with no default, so it is a catalogue change and no rewrite. It still needs ACCESS EXCLUSIVE for an instant, and that instant queues behind the longest open transaction on the table while every later query queues behind it — set lock_timeout to 2s and retry rather than wait.
- Step two, dual-write: deploy the writer that populates tenant_id on every new revision while reads still use the join. Reversible by redeploying the previous build, because nothing reads the column yet.
Follow-up
- The backfill is half finished and a rollback is required. What state is the table in, and what does the previous build do with a half-populated column?
- How do you verify the backfill actually finished, given rows are still being inserted while it runs?
What is a class?
What is a class?
Approach
- Clarify what is being asked and what a complete answer contains.
- State your assumptions explicitly before working the problem.
- Say what you would check first and why it is the highest-information step.
- 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 does an interrupt look like in a keyboard?
What does an interrupt look like in a keyboard?
Approach
- Clarify what is being asked and what a complete answer contains.
- State your assumptions explicitly before working the problem.
- Say what you would check first and why it is the highest-information step.
- 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 I design a fail-safe system to power a motor?
How would I design a fail-safe system to power a motor?
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 previous projects you've worked on.
Explain previous projects you've worked on.
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?
Teach us anything of interest to you in 5 minutes.
Teach us anything of interest to you in 5 minutes.
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?
What’s the one skill you need to be successful at this position?
What’s the one skill you need to be successful at this position?
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 adding space or scaling a resource-constrained system?
How do you approach adding space or scaling a resource-constrained system?
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 would you troubleshoot a ticket you're not familiar with?
How would you troubleshoot a ticket you're not familiar with?
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 University of Texas at Austin candidates report.
Prepare, practise & reflect
One practical outcome each day. Spend longer where you need it.
0 / 7 done01Map the University of Texas at Austin loop
- Write out the reported sequence: Phone Screen, Technical Interviews, Behavioral Assessment.
- 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.
02Work Troubleshooting / debugging
- Spend the session on Troubleshooting / debugging, which University of Texas at Austin candidates report being tested on.
- Write one worked example in Troubleshooting / debugging and time yourself on it.
Deliverable: One timed worked example in Troubleshooting / debugging.
03Work Java
- Spend the session on Java, which University of Texas at Austin candidates report being tested on.
- Write one worked example in Java and time yourself on it.
Deliverable: One timed worked example in Java.
04Work Incident/issue resolution process
- Spend the session on Incident/issue resolution process, which University of Texas at Austin candidates report being tested on.
- Write one worked example in Incident/issue resolution process and time yourself on it.
Deliverable: One timed worked example in Incident/issue resolution process.
05Answer out loud: Technical and Domain Knowledge
- Answer aloud, timed: What is a class?
- Answer aloud, timed: Tell me about your experience with Java, C, or C++.
Deliverable: Spoken answers to 2 reported Technical and Domain Knowledge question(s), under time.
06Answer out loud: Problem-Solving and System Design
- Answer aloud, timed: Explain previous projects you've worked on.
- Answer aloud, timed: Swap two values in an array.
Deliverable: Spoken answers to 2 reported Problem-Solving and System Design question(s), under time.
07Answer out loud: Behavioral and Cultural Fit
- Answer aloud, timed: Why do you want this role at the University of Texas at Austin?
- Answer aloud, timed: Discuss your work history and future career plans.
Deliverable: Spoken answers to 2 reported Behavioral and Cultural Fit 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.
Tell me about your experience with Java, C, or C++.
Tell me about your experience with Java, C, or C++.
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?
Why do you want this role at the University of Texas at Austin?
Why do you want this role at the University of Texas at Austin?
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?
Discuss your work history and future career plans.
Discuss your work history and future career plans.
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 share knowledge and support your teammates in a collaborative setting?
How do you share knowledge and support your teammates in a collaborative setting?
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?
Is there anything in your background or capabilities that you feel we should know about?
Is there anything in your background or capabilities that you feel we should know about?
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 time you collaborated across departments to resolve an operational bottleneck.
Describe a time you collaborated across departments to resolve an operational bottleneck.
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
Tell me about your experience with Java, C, or C++.
- 02
Why do you want this role at the University of Texas at Austin?
- 03
Discuss your work history and future career plans.
- 04
How do you share knowledge and support your teammates in a collaborative setting?
How difficult are the technical interviews at the University of Texas at Austin?
The technical difficulty is generally moderate and approachable compared to hyper-growth private tech companies. The emphasis is less on punishing leetcode extremes and more on verifying your practical coding competence, debugging methodology, and ability to communicate your thought process clearly.
University of Texas at Austin Software Engineer candidate reports ↗How long does the interview and hiring process typically take?
Due to institutional governance and coordination across university departments, the timeline can range from a few weeks to a couple of months from initial application to final offer. Patience and proactive, polite follow-ups with your recruiter are key.
University of Texas at Austin Software Engineer candidate reports ↗Is remote or hybrid work common for software engineers at the university?
Work arrangements depend heavily on the specific department, lab, or administrative unit hiring for the role. Many positions are based on-site in Austin, Texas, to facilitate close collaboration with research teams, though certain roles offer hybrid flexibility. Check the specific job posting details for location requirements.
University of Texas at Austin Software Engineer candidate reports ↗What is the best way to stand out during the panel interview?
Differentiate yourself by demonstrating genuine enthusiasm for the university's mission, asking insightful questions about the team's research or operational goals, and clearly articulating how your past technical projects solved tangible problems for users.
University of Texas at Austin Software Engineer candidate reports ↗Are there opportunities for career growth within the university engineering ecosystem?
Yes, engineers often transition between academic research labs, core IT infrastructure, and specialized R&D centers. The university values continuous learning, and many roles offer pathways to take on greater architectural leadership and project ownership over time.
University of Texas at Austin Software Engineer candidate reports ↗What topics does University of Texas at Austin test in interviews?
University of Texas at Austin interviews most often cover Behavioral Interviewing, Problem Solving, Stakeholder Management, Data Analysis, and Communication Skills. The exact emphasis depends on the specific role you apply for.
University of Texas at Austin 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.
- 01University of Texas at Austin 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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