A Software Engineer at UC San Francisco plays a pivotal role in advancing health worldwide. Unlike traditional tech companies, engineering at UCSF directly supports clinical care, groundbreaking biomedical research, and health education. You will build and maintain applications that help researchers analyze complex clinical data, streamline patient care workflows, and support the university's administrative and educational infrastructure. The work here is highly collaborative and mission-driven. You will often find yourself working alongside Principal Investigators (PIs), clinicians, researchers, and administrative leaders who rely on your technical expertise to solve real-world healthcare challenges. This means your code does not just drive engagement or revenue—it directly impacts patient outcomes and scientific discovery. Because UC San Francisco is a vast institution with diverse departments, the specific focus of a Software Engineer role can vary significantly. You could be developing a specialized web portal for a research lab, optimizing high-performance computing clusters for genomic analysis, or integrating enterprise health systems. This diversity of projects offers unique opportunities for technical growth and deep domain specialization.
Initial Screening
reportedAn in-house recruiter discusses your background, salary expectations, and overall fit.
What to demonstrate
- An in-house recruiter discusses your background, salary expectations, and overall fit
- Depth in Backend Development
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.
Hiring Manager Conversation
reportedA deeper discussion about your technical experience with the hiring manager.
What to demonstrate
- A deeper discussion about your technical experience with the hiring manager
- Depth in Backend Development
How to prepare
- Prepare two projects you led end to end, each with the decision you owned and what it cost.
- Have three questions about the team's roadmap and how success is measured in the first six months.
Technical Assessment
reportedA technical assessment or presentation round where you solve a specific problem.
What to demonstrate
- A technical assessment or presentation round where you solve a specific problem
- Depth in Backend Development
How to prepare
- Answer aloud and timed: Explain your familiarity with Scrum or other agile methodologies in a software development lifecycle.
- Answer aloud and timed: How would you debug an intermittent server-side error in a production environment?
Panel Interviews
reportedInterviews with key stakeholders, including department managers, clinicians, or academic researchers.
What to demonstrate
- Interviews with key stakeholders
- Including department managers, clinicians, or academic researchers
How to prepare
- Answer aloud and timed: Describe your experience with cloud hosting services and containerization tools like Docker.
- Answer aloud and timed: How do you approach setting up and managing local development environments for a distributed team?
PracHub editorial advice for the preparation topics above.
Be transparent about technical gaps
The interviewers at UCSF appreciate honesty and a growth mindset. If you encounter a question about a technology you haven't used, explain how you would go about learning it. Past candidates have noted that interviewers are highly supportive and will even offer advice on how to fill those gaps.
Highlight your mission-driven motivation
Be prepared to explain why you want to work at an academic medical center rather than a traditional commercial tech company. Showing that you value public service, healthcare innovation, or scientific research will resonate strongly with your interviewers.
Retrying a write that is not safe to repeat
A timeout tells you nothing about whether the server applied the write, so a blind retry of a create or a charge can duplicate it. Either make the operation idempotent, with a caller-supplied key the server deduplicates on or a conditional update, or do not retry it; and use exponential backoff with jitter so the retries of many clients do not synchronise into a second outage.
Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.
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?
Diff a projection against the primary without per-row point reads
The listing projection has drifted and some rows show a stale version. The primary holds 40,000,000 resource rows across 12,000 tenants while serving 1,200 writes and 14,000 reads per second. The obvious repair, reading each resource row and comparing its version against the projection, is correct and would eventually finish. Explain precisely why it is unacceptable here, then give a diff that finds the differing rows, state its complexity, and make it safe to run against a live primary. Replication lag is usually under 100 ms and is not bounded.
Approach
- Quantify the naive cost rather than calling it slow: 40,000,000 point reads at even 0.5 ms each is over five hours serialised, and the only lever is concurrency, which is exactly what you cannot spend. The primary's pool is sized for the write path, and 40,000,000 random reads evict the buffer cache that sustains the 85 percent cache hit rate, so the audit degrades the system it is auditing.
- Replace random access with one ordered pass per side. Both sides can be read in (tenant_id, resource_id) order, which is a sequential scan on each and a merge join in O(n) time and O(1) memory. For a dense diff that is the whole answer, and it reads the primary once instead of 40,000,000 times.
- For the expected sparse case, compare range hashes instead of rows: partition the key space, compute per range an order-independent aggregate over hash(resource_id, version), compare aggregates, and descend only into ranges that differ. With d differing rows and branching factor B, at most d ranges mismatch per level, so the drill-down examines O(d log_B(n/d)) ranges and reads full rows only in mismatching leaves.
- Aggregate with a sum modulo 2^64 or a multiset hash, never XOR. XOR is order-independent but self-cancelling, so two rows wrong in the same way, or a row duplicated on one side, leave the range aggregate matching and the range is declared clean.
Follow-up
- The diff reports 900 stale rows. How do you decide between patching those rows and rebuilding the projection from resource_revision?
- Same job, but the projection lives in a search index that cannot be scanned in key order. What changes?
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?
Stop tag and share joins from fanning out a page
resource_tag is (resource_id, tag_id) with PK (resource_id, tag_id); resource_share is (resource_id, shared_with_user_id, permission). The tagged-and-shared listing inner-joins resource to both, filters tenant_id, tag_id = ANY($2) and shared_with_user_id = $3, orders by updated_at DESC and takes 50. Pages come back with fewer than 50 distinct resources and the total in the header is far too high. Explain the row multiplication, rewrite both the page query and the count query so each is correct, and name the index each one needs. PostgreSQL 16.
Approach
- Do the arithmetic against the predicates that are actually there. An inner join emits one row per matching child row, and both joins are filtered: tag_id = ANY($2) admits only the requested tags, shared_with_user_id = $3 admits one user's share rows. So a resource holding three of the requested tags and shared with $3 once yields three rows, not one — the multiplier is its count of matching tags times its share rows for that single user, and that second factor is 1 unless the table admits duplicate (resource_id, shared_with_user_id) pairs. LIMIT 50 then limits rows rather than resources, and COUNT(*) counts pairs — the header is the product, not the population.
- Reject DISTINCT as the fix. It deduplicates after the product has been built, so the planner must materialise and sort the fanned-out set before the LIMIT can apply, and it leaves any SUM or AVG in the same select list wrong.
- Rewrite both filters as semi-joins, keeping resource as the only row source: AND EXISTS (SELECT 1 FROM resource_tag rt WHERE rt.resource_id = r.resource_id AND rt.tag_id = ANY($2)) and the same shape against resource_share. A semi-join stops at the first match per resource and preserves the driving index order, so ORDER BY updated_at DESC, resource_id DESC LIMIT 50 still stops after 50 rows.
- Count with the same predicates and no join at all: SELECT count(*) FROM resource r WHERE r.tenant_id = $1 AND r.status = 'active' AND EXISTS (...) AND EXISTS (...). Nothing multiplies a resource, so the number is the population.
Follow-up
- The filter changes from 'any of these tags' to 'all of these tags'. Rewrite it and state what it costs relative to the ANY form.
- A resource can be shared with the same user twice under different permissions. Does your count change, and should it?
Replace offset paging on the resource feed with keyset
resource holds resource_id, tenant_id, owner_user_id, title, body_ref, version, status ('draft','active','archived','deleted'), created_at, updated_at, deleted_at, with an index on (tenant_id, status, updated_at DESC, resource_id DESC). The listing endpoint returns active resources for one tenant, newest update first, 50 per page, today with LIMIT 50 OFFSET n. Tenants reach page 400 and rows are created while they read. Write the keyset query, define what the cursor carries and how it is encoded, and say which part of the index each predicate uses. Assume PostgreSQL 16.
Approach
- Name the two failures separately. OFFSET 20000 makes the server produce and discard 20,000 rows, so page cost grows with depth rather than with page size. Independently, any write that changes how many rows sort above the offset moves the window between two fetches, and the direction decides which anomaly you get: an insert lands at the head of updated_at DESC and pushes already-returned rows down past the boundary, so they are returned a second time; a delete above the offset, or a row whose updated_at is bumped above the cursor, pulls rows up and one is never returned at all. Nothing in the response reveals either.
- Write the seek: WHERE tenant_id = $1 AND status = 'active' AND (updated_at, resource_id) < ($2, $3) ORDER BY updated_at DESC, resource_id DESC LIMIT 50. The row-value comparison is one index range rather than a disjunction, and both columns are NOT NULL, which is what makes that comparison well defined.
- Map each predicate onto the index: tenant_id and status are equality on the leading columns, (updated_at, resource_id) is the range, and the ORDER BY matches the index order so no Sort node appears and the scan stops after 50 rows. The DESC in the definition only matters for mixed directions — a plain ascending btree on the same columns is read backwards for this query.
- Put both sort columns in the cursor and nothing the client can tamper with into another tenant: base64 of (updated_at, resource_id), validated server-side, with tenant_id taken from the principal.
Follow-up
- The client asks for 'jump to page 400'. What do you offer instead, and what does the honest version cost?
- Sort order becomes user-selectable across four columns. How many indexes is that, and which would you refuse to add?
How do you manage state and data flow between a backend API and a frontend user interface?
How do you manage state and data flow between a backend API and a frontend user interface?
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 is your approach to optimizing database queries for a high-traffic web application?
What is your approach to optimizing database queries for a high-traffic web application?
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 do you ensure your code complies with modern security standards, especially when handling sensitive data?
How do you ensure your code complies with modern security standards, especially when handling sensitive data?
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?
Explain your familiarity with Scrum or other agile methodologies in a software development lifecycle.
Explain your familiarity with Scrum or other agile methodologies in a software development lifecycle.
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 do you approach setting up and managing local development environments for a distributed team?
How do you approach setting up and managing local development environments for a distributed team?
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?
What steps do you take to automate routine system maintenance tasks?
What steps do you take to automate routine system maintenance tasks?
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 you debug an intermittent server-side error in a production environment?
How would you debug an intermittent server-side error in a production environment?
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 UC San Francisco candidates report.
Prepare, practise & reflect
One practical outcome each day. Spend longer where you need it.
0 / 7 done01Map the UC San Francisco loop
- Write out the reported sequence: Initial Screening, Hiring Manager Conversation, Technical Assessment, Panel Interviews.
- For each round, write one sentence on what it is judging, from the description above, and mark the one you are least ready for.
Deliverable: A one-page map of the 4 reported rounds, with the weakest marked.
02Work Backend Development
- Spend the session on Backend Development, which UC San Francisco candidates report being tested on.
- Write one worked example in Backend Development and time yourself on it.
Deliverable: One timed worked example in Backend Development.
03Work Scrum / Agile Methodology
- Spend the session on Scrum / Agile Methodology, which UC San Francisco candidates report being tested on.
- Write one worked example in Scrum / Agile Methodology and time yourself on it.
Deliverable: One timed worked example in Scrum / Agile Methodology.
04Work Software Engineering
- Spend the session on Software Engineering, which UC San Francisco candidates report being tested on.
- Write one worked example in Software Engineering and time yourself on it.
Deliverable: One timed worked example in Software Engineering.
05Answer out loud: Technical & Core Engineering
- Answer aloud, timed: How do you manage state and data flow between a backend API and a frontend user interface?
- Answer aloud, timed: Describe your experience working with CI/CD pipelines and how you handle deployment automation.
Deliverable: Spoken answers to 2 reported Technical & Core Engineering question(s), under time.
06Answer out loud: Systems & Administration
- Answer aloud, timed: How would you debug an intermittent server-side error in a production environment?
- Answer aloud, timed: Describe your experience with cloud hosting services and containerization tools like Docker.
Deliverable: Spoken answers to 2 reported Systems & Administration question(s), under time.
07Answer out loud: Behavioral & Collaboration
- Answer aloud, timed: How do you prefer to be managed, and what environment allows you to do your best work?
- Answer aloud, timed: Tell me about a time you mentored a junior team member or helped a colleague overcome a technical hurdle.
Deliverable: Spoken answers to 2 reported Behavioral & Collaboration question(s), under time.
Expand any day for tasks and deliverables. Your progress is saved on this device.
Behavioural rounds judge the decision you made and what it cost.
Describe your experience working with CI/CD pipelines and how you handle deployment automation.
Describe your experience working with CI/CD pipelines and how you handle deployment automation.
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 your experience with cloud hosting services and containerization tools like Docker.
Describe your experience with cloud hosting services and containerization tools like Docker.
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 prefer to be managed, and what environment allows you to do your best work?
How do you prefer to be managed, and what environment allows you to do your best work?
Approach
- Pick a story where you made the decision, not one where you watched it.
- State the situation in two sentences and spend the rest on the reasoning.
- Give the blast radius: what could have broken, and what you measured.
- Name the disagreement and how you resolved it with evidence.
Follow-up
- What would you do differently if you ran that again?
- How did you know your change caused the improvement?
Tell me about a time you mentored a junior team member or helped a colleague overcome a technical hurdle.
Tell me about a time you mentored a junior team member or helped a colleague overcome a technical hurdle.
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?
What steps do you actively take for your own professional development and to keep your skills current?
What steps do you actively take for your own professional development and to keep your skills current?
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 situation where you had to work with a stakeholder who did not have a technical background. How did
Describe a situation where you had to work with a stakeholder who did not have a technical background. How did you communicate technical concepts to them?
Approach
- Pick a story where you made the decision, not one where you watched it.
- State the situation in two sentences and spend the rest on the reasoning.
- Give the blast radius: what could have broken, and what you measured.
- Name the disagreement and how you resolved it with evidence.
Follow-up
- What would you do differently if you ran that again?
- How did you know your change caused the improvement?
How do you handle conflicting priorities when multiple stakeholders are requesting updates to your systems?
How do you handle conflicting priorities when multiple stakeholders are requesting updates to your systems?
Approach
- Pick a story where you made the decision, not one where you watched it.
- State the situation in two sentences and spend the rest on the reasoning.
- Give the blast radius: what could have broken, and what you measured.
- Name the disagreement and how you resolved it with evidence.
Follow-up
- What would you do differently if you ran that again?
- How did you know your change caused the improvement?
- 01
Describe your experience working with CI/CD pipelines and how you handle deployment automation.
- 02
Describe your experience with cloud hosting services and containerization tools like Docker.
- 03
How do you prefer to be managed, and what environment allows you to do your best work?
- 04
Tell me about a time you mentored a junior team member or helped a colleague overcome a technical hurdle.
How difficult is the Software Engineer interview at UCSF?
The difficulty is generally rated as average, but it varies by department. Some academic research labs may focus heavily on specialized domain knowledge, while central IT departments tend to focus on standard enterprise software engineering practices and systems integration.
UC San Francisco Software Engineer candidate reports ↗How long does the hiring process typically take?
Because UCSF is a public institution with thorough background checks and HR protocols, the process is notoriously slow. It can take anywhere from several weeks to a few months from your initial phone screen to your first day on the job.
UC San Francisco Software Engineer candidate reports ↗Are there remote or hybrid work opportunities?
Yes, many software engineering roles at UCSF support hybrid or fully remote work arrangements, depending on the specific department and the nature of the systems you support. This should be discussed during your initial recruiter screen.
UC San Francisco Software Engineer candidate reports ↗What is the work culture like for engineers at UCSF?
The culture is highly collaborative, mission-driven, and supportive. Unlike fast-paced startup environments, there is a strong emphasis on work-life balance, professional development, and contributing to the public good through healthcare and research.
UC San Francisco Software Engineer candidate reports ↗What topics does UC San Francisco test in interviews?
UC San Francisco interviews most often cover Problem Solving, Adaptability, Stakeholder Management, Panel Interviewing, and Stakeholder Communication. The exact emphasis depends on the specific role you apply for.
UC San Francisco 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.
- 01UC San Francisco 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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