As a Software Engineer at Maven Clinic, you play a crucial role in developing and maintaining technology solutions that directly impact women’s health and wellness. This position matters for the functionality of Maven's products, for the user experience, and for keeping Maven's services accessible and effective. You will be part of a team that builds scalable systems, implements innovative features, and drives the evolution of digital healthcare.
In this role, you will engage with complex problems that reflect the unique challenges in the healthcare domain. You will work on diverse projects, from appointment scheduling applications to backend services that support Maven Clinic's telehealth solutions. Your contributions will influence how users interact with Maven Clinic's platform, which is central to the company's mission-driven work. Expect to collaborate with cross-functional teams, including product managers and designers, to create solutions that are not only technically sound but also user-centric.
This position offers the opportunity to apply your technical skills in a meaningful way, allowing you to directly impact the lives of users and contribute to the broader mission of improving healthcare for women and families.
Phone Screening
reportedHalf of this call is the part candidates treat as small talk: start date, notice period, work authorisation and its timing, location and time zone, on-call, and the number. Those are what kill offers late, after several engineers have each spent a day. Surfacing a hard constraint now costs you nothing and occasionally buys you something, since a loop compressed to fit a competing deadline can usually only be arranged if it is asked for early. The common failure is deflecting the compensation question twice, then discovering at offer stage that the band never reached your number.
What to demonstrate
- Whether your hard constraints are compatible with the role before a loop gets booked: earliest start, notice period, what authorisation you hold and when it needs action, days on site, willingness to carry a pager
- Whether you give a compensation range with something behind it, such as current total compensation or a competing timeline, rather than leaving the band untested
- Whether your stated timeline is real, since a competing deadline raised now is something scheduling can sometimes work around and the same deadline raised at offer stage usually is not
How to prepare
- Write each constraint down in one line before the call and state them as facts rather than negotiating them live under a question you were not expecting
- Set your range from two or three current data points for that level and location, and name the structure you are quoting in, so the number is comparable to the one they are holding
- If another process is running, say where it stands and by when, and ask directly whether this loop can be scheduled inside that window
Coding Assessment
reportedInput bounds are the part of the prompt most often skimmed, and they usually contain the answer. They tell you which complexity class is admissible, which narrows the search before you have thought about the problem itself. As a rough planning figure, a compiled language does on the order of 10^8 simple operations per second and an interpreted one roughly an order of magnitude less. So n up to about twenty admits enumerating subsets, a few thousand admits a quadratic pass, and a million admits neither: you need near-linear, or linear with a log factor. If the bounds are missing, ask for them.
What to demonstrate
- Whether the approach is justified by the stated input size rather than by whichever pattern you recognised first
- Whether you ask about the properties that change the algorithm: whether the input arrives sorted, whether duplicates occur, whether values are bounded integers, whether it all fits in memory
- Whether you can name the bottleneck in your own solution and what would remove it, even when you deliberately leave it in place
- Whether a claimed speedup is real, since memoising a recursion only helps when subproblems genuinely overlap and the state can be keyed cheaply
How to prepare
- For each algorithm you rely on, write down the largest n it handles in roughly a second, then check two of those figures by timing them in the language you will actually type in
- For two weeks, write one line naming your target complexity and the bound that justifies it before you write any code, then compare that line with what you ended up submitting
- Practise the conversion backwards: given a required O(n log n), list the mechanisms that get you there (sorting, a heap, an ordered map, divide and conquer) and choose by what the problem needs to query, not by what you used last
Technical Interviews
reportedMost of the time lost in this format is not lost to thinking. It goes to a standard-library call you half-remember, an off-by-one in a loop bound, and a debugging loop that mutates code at random until something passes. When output is wrong, stop re-reading the whole function: take the smallest input that reproduces it and walk the state through by hand, printing intermediates if the environment allows. Guessing at a fix without a failing case you understand is how a five-minute bug becomes twenty, and the clock does not pause while you do it.
What to demonstrate
- Whether you reach the right structure without a detour, and can write it from memory rather than only recall that one exists
- Whether overflow is considered where the language has fixed-width integers, since a signed 32-bit value stops at 2,147,483,647 and then wraps in Java, is undefined behaviour in C++, and does not arise in Python, whose integers grow instead
- Whether recursion depth is treated as a constraint on large inputs, given that CPython's default limit is 1000 frames and a deep recursion can exhaust the stack in any language where an iterative version would not
- Whether a failing case is isolated and explained before any edit is made to the code
How to prepare
- From an empty file and with no references open, implement the pieces you lean on most: a heap push and pop, an iterative DFS with an explicit stack, and a binary search whose midpoint is written lo + (hi - lo) / 2, which avoids the overflow that (lo + hi) / 2 can hit in a fixed-width integer type
- Time yourself on the ten library calls you look up most, such as sorting with a custom comparator, splitting and joining strings, and finding the next key at or above a value in an ordered map, until the lookup is gone
- Take a solution you know is broken and, before touching it, write one sentence naming the input, the expected value and the actual value. Repeat until you do it without deciding to.
Behavioral Assessments
reportedWhat you say here is written down by each interviewer and compared afterwards, so the unit of evaluation is a claim someone else could check, not a well-told narrative. Two things make a story checkable: detail only a participant would hold, and a clean line around which part was yours. Vague ownership is the usual failure and it is usually accidental, because engineers say we about the team's work and we about their own, so the thing they personally built disappears into the plural. Name the part you wrote, and name who did the rest.
What to demonstrate
- Whether your details are ones a participant would hold and an observer would not: the constraint that ruled out the obvious approach, the first attempt that failed, the person who objected and on what grounds
- Whether ownership survives a direct question, since a follow-up to we decided is routinely who decided, and an answer that stays plural at that point is read as the work belonging to someone else
- Whether the numbers you quote are ones you would say identically to a former colleague with the dashboard open
How to prepare
- Go through each story replacing every we with either I or a named role (the on-call engineer, the reviewer, the other team) and check the story still holds together. Wherever it stops making sense you have found a part you cannot actually speak to
- Open the artefacts for two of your stories, the pull request, the design doc, the incident notes, and read them for dates and figures you have been rounding in the retelling. Correct your version to match
- For each story write the single sentence you would least want repeated to a former teammate, then either make it accurate or take it out
PracHub editorial advice for the preparation topics above.
Treating a medical record number or a member ID as a globally unique key and joining on it directly.
These identifiers are unique only within the authority that issued them. Two facilities in one network routinely have the same medical record number for different people, and member IDs get reissued when someone changes plans. Joining on the bare value merges two patients' records, which is the most damaging failure available in this domain, and it passes every test written against a single-facility fixture because the collision only appears once a second source is connected.
Writing the access audit record inside the read transaction, or firing it off after the response with no durability.
Inside the transaction, an audit-store outage blocks clinical reads and turns a logging dependency into a care outage. Fire-and-forget afterwards means the audit trail is incomplete during precisely the incidents it exists to reconstruct, and the gaps are invisible until someone asks for the log. The usual resolution is committing the access decision and its audit row together to a local outbox and shipping asynchronously, which keeps the read path available while preserving durability.
Never running a concrete value through the code
Trace one small input and one edge input by hand, index by index, out loud. Re-reading your own code catches design mistakes; walking a real value through it catches the off-by-one, the uninitialised accumulator and the loop that never advances.
A queue or buffer with no bound
Every producer-consumer boundary needs a capacity and a policy for reaching it: block the producer, shed load, or drop the oldest entry. Unbounded buffering converts a temporary slowdown into memory exhaustion and hides the backpressure signal that would have revealed the consumer was falling behind.
Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.
What data structures would you use to implement a caching mechanism?
What data structures would you use to implement a caching mechanism?
Approach
- Name the brute-force solution and its complexity before improving on it.
- Choose the data structure from the access pattern, not from familiarity.
- Restate the input: its shape, its size, and what is guaranteed about it.
Follow-up
- How does this change if the input no longer fits in memory?
- Which test case would catch an off-by-one here?
Describe how you would test your code.
Describe how you would test your code.
Approach
- Walk one small example through your approach before writing the whole thing.
- Restate the input: its shape, its size, and what is guaranteed about it.
- State the target complexity and say which constraint rules the naive version out.
Follow-up
- What is the worst case, and how likely is it on real data?
- How does this change if the input no longer fits in memory?
How would you implement a function to check for balanced parentheses?
How would you implement a function to check for balanced parentheses?
Approach
- Walk one small example through your approach before writing the whole thing.
- Name the brute-force solution and its complexity before improving on it.
- Restate the input: its shape, its size, and what is guaranteed about it.
Follow-up
- How does this change if the input no longer fits in memory?
- Which test case would catch an off-by-one here?
Can you explain the time complexity of your solution for a given probl…
Can you explain the time complexity of your solution for a given problem?
Approach
- Choose the data structure from the access pattern, not from familiarity.
- State the target complexity and say which constraint rules the naive version out.
- Name the brute-force solution and its complexity before improving on it.
Follow-up
- What is the worst case, and how likely is it on real data?
- Which test case would catch an off-by-one here?
Sum surviving claim versions in one pass over unordered lines
You are streamed up to 50 million claim_line records in arbitrary order: claim_id, claim_version, line_number, frequency_code (original, replacement, void), enterprise_person_id, allowed_amount_cents. Every version of a claim shares its claim_id, and a replacement arrives as a higher claim_version. Return total allowed_amount_cents per enterprise_person_id, counting each claim once at its highest version and contributing zero when that version is a void. Amounts are non-negative int64 minor units. Target O(N) time and O(C) space for C distinct claims, single pass, no sort.
Approach
- Say the two grains out loud before writing anything: the version lives at claim grain, the money lives at line grain. Every wrong answer here comes from applying a claim-level rule with a line-level filter.
- Keep one hash map from claim_id to a small record (best_version, sum_cents, person_id, is_void). Per line: if claim_version is greater than best_version, reset sum_cents to this line's amount and overwrite the person and void flag; if equal, add to it; if lower, discard the line. That reset is what makes the pass order-independent, so a replacement arriving before its original still wins.
- Resolve the void at the end, not at ingest. A void only nullifies the claim if it is the surviving version, and zeroing on sight would let an earlier replacement that arrives later resurrect the money.
- Fold the C claim records into a person-keyed map in a second phase, O(C). Do not accumulate into the person total during the stream: you cannot subtract a superseded version you have already forgotten.
- Cost is O(N) time, O(C) space. The sort-based alternative, group by (claim_id, claim_version) and keep the max, is O(N log N) and needs the set resident; prefer it only when C approaches N and the map will not fit.
- Keep cents in int64 throughout. Fifty million lines at a realistic per-line ceiling stay four orders of magnitude below 2^63, so the integer type costs nothing and removes the float drift class entirely.
Worked solution 20 min
- Define the per-claim record and the three-way comparison on claim_version: greater resets, equal accumulates, lower discards.
- Run the stream once, touching only the claim map.
- Walk the claim map, skipping entries whose surviving version is a void, and add each remaining sum into a person map.
- Return the person map sorted descending by total.
Follow-up
- You shard the stream across eight workers. Sharding by hash of claim_id works; what exactly breaks if you shard by enterprise_person_id instead?
- A replacement arrives for a claim_id whose original never appears in the batch. What does your map produce, and is that the financially correct answer or a reconciliation break?
- The same claim_id is reused by two different submitting organisations. How does the key have to change, and when would you have noticed?
Fix a paid-amount report that double counts claim versions
claim_line: claim_line_id, claim_id, claim_version smallint, line_number, frequency_code in ('original','replacement','void'), enterprise_person_id, coverage_id, billing_provider_npi, service_from_date, procedure_code, units, billed_amount_cents bigint, allowed_amount_cents, paid_amount_cents, adjudication_status, UNIQUE (claim_id, claim_version, line_number). A monthly report sums paid_amount_cents and joins coverage_span on enterprise_person_id with date overlap to attribute a plan. It overstates payment by roughly 9%, and reconciliation against remittance fails. Name both causes, then write the corrected query returning paid cents and plan per billing_provider_npi for March 2026 service dates.
Approach
- Cause one: the SUM spans every claim version. A replacement supersedes a prior claim and a void cancels one, so a replaced claim contributes twice and a voided claim contributes once when it should contribute nothing. Resolve the surviving version per claim_id before aggregating, never by deleting superseded rows.
- Cause two: the coverage join is one-to-many. coverage_span carries a system-time version per coverage period, so a person with three beliefs about one period multiplies every claim line by three. The join is for a label, not for filtering, so it must not be allowed to change cardinality.
- Collapse the version chain with DISTINCT ON (claim_id) ... ORDER BY claim_id, claim_version DESC, then join the lines back on (claim_id, claim_version), and exclude the surviving version when its frequency_code is 'void'.
- Make the coverage lookup cardinality-safe with a LEFT JOIN LATERAL ... LIMIT 1 pinned to valid_to = 'infinity' and ordered by valid_from DESC. LATERAL with LIMIT 1 cannot fan out by construction; a plain join can, and no amount of later DISTINCT repairs a SUM that already doubled.
- Keep the money in integer minor units the whole way. SUM(bigint) returns numeric in PostgreSQL, so there is no overflow and no binary floating-point error; cast to a display type only at the edge.
Worked solution 35 min
- Prove cause one with a diagnostic: SELECT claim_id, count(DISTINCT claim_version) FROM claim_line GROUP BY 1 HAVING count(DISTINCT claim_version) > 1 — confirm the affected claims exist and their paid totals.
- Prove cause two by running the original report with count(*) alongside the SUM and comparing it to the count of qualifying claim_line rows; any excess is fan-out.
- Write the surviving-version CTE with DISTINCT ON (claim_id).
- Join the lines to it on both claim_id and claim_version, exclude 'void', filter adjudication_status = 'paid' and the half-open March service-date range.
- Attach the plan label through LEFT JOIN LATERAL with LIMIT 1, then re-run the count(*) check and confirm it now equals the qualifying line count exactly.
- Diff the new total against remittance and confirm the 9% gap closes to zero cents, not to 'close enough'.
Follow-up
- A replacement arrives before its original. Your surviving-version logic picks max(claim_version) — what does the report show in the window before the original lands, and does the number self-correct?
- Add a claim_line_adjustment table with several reason codes per line. What does that do to this query, and where do you aggregate to keep it safe?
- Reconciliation is still off by 400 cents. Which of the two causes could still produce that, and what is your next query?
Return the displayable version of each analyte with a window function
observation_result is append-only: observation_id, enterprise_person_id, encounter_id, accession_id, placer_order_id, filler_order_id, loinc_code, value_numeric, unit_ucum, result_status in ('registered','preliminary','final','amended','corrected','entered_in_error'), collected_ts, issued_ts, version, supersedes_observation_id. A chart view needs the currently displayable value for each (accession_id, loinc_code) for one person over the last 90 days, excluding analytes whose newest version is entered_in_error but keeping analytes whose older versions were. Write the query, give the index that serves it, and say honestly which part of the plan the index cannot remove.
Approach
- Recognise the shape: this is a top-1-per-group over a version chain, not a filter. Pick the newest version first, then apply the status predicate to the winner — the order is the whole exercise.
- Use DISTINCT ON (accession_id, loinc_code) with ORDER BY accession_id, loinc_code, version DESC, observation_id DESC on PostgreSQL; the ORDER BY must start with the DISTINCT ON expressions or it is a syntax error. Use ROW_NUMBER() OVER (PARTITION BY ... ORDER BY version DESC, observation_id DESC) with an outer WHERE rn = 1 if you need portability, since a window function cannot be referenced in the WHERE of its own query level.
- Push only person and time into the inner WHERE. Pushing result_status <> 'entered_in_error' inside promotes a retracted result's predecessor back onto the chart, which is the bug the prompt is testing for.
- Index (enterprise_person_id, collected_ts DESC) so the 90-day slice is an index range scan rather than a scan of the person's whole history.
- Be straight about the limit: because collected_ts carries a range predicate, no b-tree index can also deliver rows pre-ordered by (accession_id, loinc_code, version DESC), so the sort stays in the plan. That is acceptable because one person's 90-day slice is hundreds to low thousands of rows, and it is a quicksort in work_mem rather than an external merge — verify that rather than assume it.
Follow-up
- A correction arrives carrying the same filler_order_id and the same version number as the row it corrects. What breaks, and what do you add to the sort key?
- The same query for a cohort of 50,000 people instead of one person: does DISTINCT ON still hold up, and at what point do you materialise a latest-version table with a partial index?
- How would you show the clinician that the displayed value was amended, given the chart query returns only the winner?
Discuss how you would handle user authentication in a distributed syst…
Discuss how you would handle user authentication in a distributed system.
Approach
- Name the failure you are designing for, then the recovery path.
- 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.
Follow-up
- What would you drop to keep the system up under load?
- How does this behave when that dependency is down for an hour?
How do you ensure data integrity in your designs?
How do you ensure data integrity in your designs?
Approach
- Choose a partition key and say what query it makes expensive.
- State the consistency you need, and where you are willing to be stale.
- Name the failure you are designing for, then the recovery path.
Follow-up
- How does this behave when that dependency is down for an hour?
- What would you drop to keep the system up under load?
You are given a dataset of user appointments. How would you analyze it…
You are given a dataset of user appointments. How would you analyze it to improve scheduling efficiency?
Approach
- Say what you would check first and why it is the highest-information step.
- Clarify what is being asked and what a complete answer contains.
- State your assumptions explicitly before working the problem.
Follow-up
- How would you know your answer was wrong?
- What assumption would you test first?
Set rate limits across registration, batch ingest and bulk export
Three callers share the identity resolution and longitudinal record APIs: a registration desk making 1-3x10^3 interactive match calls a second against a 200ms budget, a nightly ingest replaying millions of messages in a burst, and partner bulk exports walking whole panels. One global limit either starves registration during the batch window or stretches the batch past morning. Define the limiting scheme - the key, the algorithm, the reserved capacity, the response when a caller is limited - and state precisely what each of the three callers does when it is limited.
Approach
- Reject a single global limit, then reject per-IP: the batch runs from a handful of hosts and the desk sits behind a shared egress, so IP is simultaneously too coarse and too fine. Key on the authenticated principal plus a traffic class bound to the credential, never a class the caller declares in a header it controls.
- Pick the algorithm from the traffic shape. A fixed-window counter admits up to twice the limit across a window boundary, which is exactly the top-of-hour moment the desk bursts; a token bucket with a burst allowance, or a sliding-window counter, does not. The desk needs burst tolerance; the batch needs a steady ceiling.
- Give the classes different treatment rather than different numbers of the same thing: a reserved floor of capacity the interactive class cannot be pushed below, the remainder shared by batch and export, and the batch class shedding first under pressure. This is a scheduling decision - the class that can wait is the one that waits.
- Answer with 429 plus Retry-After in delta-seconds and the limit, remaining and reset headers, so the caller need not invent a backoff. Keep 429 distinct from 503: 429 means try later and the request did no work, 503 means the service is unhealthy. Conflating them makes correct client behaviour impossible to write.
- Write each caller's behaviour, because a limit without a documented client response only relocates the failure. The desk degrades to deterministic-only matching and flags the registration for review rather than blocking a patient; the ingest applies backpressure to its consumer, since the messages are durable and delay is free while loss is not; the export sleeps Retry-After and resumes from its cursor. Then state where the counter lives: a shared store with one atomic increment-and-expire per decision, sized so the limiter is not the bottleneck at tens of thousands of decisions a second, and with a stated fail-open or fail-closed behaviour when it is unreachable.
Worked solution 25 min
- Write the three traffic profiles as numbers: request rate, burst shape, latency budget, and what each caller can tolerate on refusal.
- Choose the key, show where the class comes from on the credential, and write the check that stops a caller self-declaring.
- Work the fixed-window boundary arithmetic that admits twice the limit, then the token-bucket or sliding-window version that does not.
- Write the full 429 response with every header, and the exact backoff each of the three callers implements.
- Write the reserved-capacity rule and trace all three classes at 150 percent of total demand.
Follow-up
- The batch runs under the same credential as an interactive tool. How do you separate them?
- The limiter's shared store goes down. Does traffic fail open or closed, and what is the argument for your choice here specifically?
- One partner's export is now 40 percent of read load while staying inside its limit. What do you change?
Gateway instances OOM every five days with flat traffic
Interoperability gateway instances are OOM-killed with exit 137 every five to six days. Resident memory grows about 400MB a day per instance and never falls; restarting resets it. Message throughput, connection count and partner mix are unchanged over that period. The gateway keeps an in-process set of recently seen idempotency keys — (sending facility, sending application, message control ID, event timestamp) — in front of the durable ledger. Give the ordered investigation and say what evidence would rule the idempotency cache out.
Approach
- Establish that this is a retained-object leak before profiling. Compare resident memory with live heap measured immediately after a forced collection, sampled daily. Resident memory rising while post-collection live heap stays flat points at allocator fragmentation, off-heap or native buffers, or thread stacks, and a heap dump will implicate the wrong object. Rising post-collection live heap is a genuine leak.
- Find what the growth is linear in. Plot daily growth against messages processed, connections accepted and wall-clock hours across instances carrying different traffic. Proportional to messages means something is retained per message; proportional to connections means per-connection state is not released on abnormal close, such as a half-open socket whose FIN never arrives.
- Diff two heap dumps taken six hours apart on the same instance, sorted by retained size, and identify the dominator. An unbounded set or map shows up as one root holding millions of small entries. This is a five-minute answer once you have the dumps, which is precisely why the first two steps exist: they tell you whether the dump can answer the question at all.
- Check the idempotency cache directly against arithmetic rather than suspicion. At single-digit millions of messages a day and a key of roughly 80 bytes plus per-entry overhead, an unevicted set grows by hundreds of megabytes a day, which matches the observed 400MB. A live gauge of its entry count that stays flat is the evidence that rules it out; a gauge tracking messages processed convicts it.
- Fix and restore the invariant: bound the cache by size or by a time window, and keep the durable unique constraint as the authority. An in-process set is a latency optimisation and cannot be the correctness mechanism, because it does not survive a restart and is not shared across instances — so a replay right after a deploy would bypass it entirely.
- Verify over 48 hours that post-collection live heap is flat, and alert on the cache's entry-count gauge rather than on resident memory, which only tells you after the growth has already happened.
Follow-up
- How long must the time window be? Tie the number to the sender's replay behaviour after an interface restart and to how far back the broker redelivers.
- If growth had tracked connections instead of messages, what would you look at first, and how would a half-open MLLP connection show up in the socket table?
For someone who has spent the last few years shipping features and reading other people's code, and who has not solved a timed problem from a blank file in a long time. Five days rebuild the primitives and the patterns that sit on them, working from invariants rather than remembered solutions, and the last two attach that back to the rest of the loop.
Prepare, practise & reflect
One practical outcome each day. Spend longer where you need it.
0 / 7 done01Rebuild the primitives by implementing them
- Implement a dynamic array with doubling growth and an operation counter, then change the growth rule to add a fixed sixteen slots instead, and time both for n of ten thousand, a hundred thousand and a million. The fixed-increment version resizes n/16 times at O(n) each, so its total work is quadratic; doubling is what makes append amortised constant.
- Implement a hash map with separate chaining and a load-factor resize, then insert ten thousand keys engineered to land in one bucket and record what happens to lookup time, so that average-case O(1) becomes a claim with a stated precondition rather than a reflex.
- For dynamic-array append and hash-map insert, write down which cost is amortised rather than worst-case, which single operation pays the whole bill, and what a system with a hard per-operation deadline would have to do instead.
Deliverable: Two working implementations plus a timing table showing the input at which each structure's advertised complexity stops holding.
Practice prompt ↗Practice prompt ↗Practice prompt ↗Worked solution ↗02Arrays under an invariant: two pointers, sliding window, binary search
- Solve longest-subarray-with-sum-at-most-K using a sliding window, then run it on an input containing negative numbers and watch it return the wrong answer: extending the window only moves the sum monotonically when every element is non-negative, and that precondition is the whole reason the technique works.
- Write the binary search that finds the first index satisfying a predicate rather than an exact value, put the loop invariant above the loop in a comment, and verify termination on the two inputs that break careless versions: the empty range, and a range where every element satisfies the predicate.
- Compute the midpoint as lo + (hi - lo) / 2 and write one line on why the obvious (lo + hi) / 2 is a genuine defect in a fixed-width integer type and a non-issue in a language with arbitrary-precision integers.
Deliverable: Three solved problems, each with its invariant written above the loop, plus one recorded input on which the sliding window is provably wrong.
Practice prompt ↗Practice prompt ↗03Sorting, heaps, and the greedy argument that has to be proved
- Solve one top-k problem three ways, by full sort, by a size-k heap, and by quickselect, then write the values of n and k at which each becomes the right choice, along with quickselect's quadratic worst case and why a randomised pivot makes that unlikely rather than impossible.
- Implement bottom-up heapify and count sift-down steps to confirm it does linear work rather than n log n, because most nodes sit near the bottom of the tree and therefore move only a short distance.
- Take interval scheduling by earliest finishing time and write the exchange argument out in full: given any optimal schedule, swapping in the earliest-finishing interval keeps it feasible and no smaller. Then construct the weighted variant where that same greedy fails and name what has to replace it.
Deliverable: A three-way top-k comparison with measured crossover points, one written exchange argument, and one counterexample to a greedy rule that looks almost identical.
Practice prompt ↗Practice prompt ↗04Recursion, memoisation, and the step to a table
- Take one problem with overlapping subproblems, such as edit distance or coin change, instrument the plain recursion with a call counter to show the blow-up, then add memoisation and re-count.
- Convert the memoised version to a bottom-up table and state the two properties you relied on: each subproblem's result depends only on its arguments, and the dependencies form a DAG you can enumerate in order.
- Rewrite one deep recursion with an explicit stack, then find the input length at which the original hits the interpreter's frame limit, which defaults to about a thousand frames in CPython, so you know when the rewrite is required rather than decorative.
Deliverable: One problem in three forms, naive, memoised and tabulated, with call counts for each and the input length at which recursion depth becomes the binding constraint.
Practice prompt ↗Practice prompt ↗Worked solution ↗05Graphs, where most of the work is choosing the traversal
- Implement BFS and DFS over one adjacency list, then answer for each which finds a shortest path in an unweighted graph and which you would use to detect a cycle in a directed graph, including why the in-progress versus finished distinction matters for the second.
- Implement topological sort by in-degree, feed it a graph containing a cycle, and confirm the failure signature is that fewer than V nodes come out rather than an exception, then note that the order it produces is one of several valid ones.
- Run a shortest-path search on a graph with a single negative edge weight and show the wrong answer, then write the precondition Dijkstra actually needs, non-negative weights, because it finalises a node's distance the first time that node is popped, and name the algorithm you would switch to and its own limit.
Deliverable: A small graph library with BFS, DFS and topological sort, plus two inputs that produce documented wrong answers under the wrong algorithm choice.
Practice prompt ↗Practice prompt ↗06One day for everything that is not an algorithm
- Sketch one system only to the depth a coding-heavy loop tends to reach: the endpoints, what the service stores, and the single query pattern that decides the schema. Stop at twenty-five minutes.
- Prepare the project answer for an interviewer who codes, which means rehearsing the two levels they push to: the specific thing you built, and why you chose that approach over the alternative they will name. Open with a number and be ready to say what it excludes.
- Prepare the answer to what you would do differently, choosing a real technical mistake with a specific fix rather than a complaint about process or staffing.
Deliverable: One design sketch at endpoint-and-schema depth, plus a project answer rehearsed to two levels of follow-up.
Practice prompt ↗Practice prompt ↗07Solve out loud, under time
- Do three timed problems at twenty-five minutes each in a plain editor with no autocomplete and no execution until the end, then tally separately the failures that were syntax and the ones that were approach, because those two numbers call for different fixes.
- Narrate one solution from the first sentence, stating the approach and its complexity before writing any code, and rehearse the sentence you will use when you realise mid-solution that the approach is wrong.
- Re-solve from blank the two problems you were slowest on this week and compare the times against the day they first appeared.
Deliverable: A recording of one fully narrated solution and a tally that separates syntax failures from approach failures.
Practice prompt ↗Practice prompt ↗Worked solution ↗Expand any day for tasks and deliverables. Your progress is saved on this device.
Engineers over-index on what they repaired. A stronger answer covers something you knowingly left broken: the alert you tuned down, the data inconsistency you documented instead of chasing, the cleanup you deferred past two quarters. Give the reasoning and the condition that would have reopened it, so it reads as a decision and not as neglect.
Describe a time when you optimized a piece of code. What approach did …
Describe a time when you optimized a piece of code. What approach did you take?
Approach
- Give the blast radius: what could have broken, and what you measured.
- Pick a story where you made the decision, not one where you watched it.
- Close with what you would do differently, concretely.
Follow-up
- What did you decide not to do, and why?
- What would you do differently if you ran that again?
What motivates you to work in the healthcare technology field?
What motivates you to work in the healthcare technology field?
Approach
- Name the disagreement and how you resolved it with evidence.
- Give the blast radius: what could have broken, and what you measured.
- State the situation in two sentences and spend the rest on the reasoning.
Follow-up
- What did you decide not to do, and why?
- What would you do differently if you ran that again?
Ship an eligibility cache you already know is too stale
You shipped the eligibility service against a fixed go-live with a 24-hour cache keyed on person and plan, no service date in the key and no computed-as-of field in the response, because the real-time payer inquiry path was two sprints behind. Enrolment files routinely terminate coverage retroactively — a file received on the fifth can terminate coverage effective the first. Describe shipping a compromise of this shape: what you wrote down, who accepted the risk, the bound you could honestly state, the measurement that sized the debt in money, and whether it was ever paid.
Approach
- The probe is whether you can state a compromise as a bounded risk rather than as a known limitation. Start by separating two staleness sources, because they have different fixes: cache staleness is bounded by the TTL, while business-time staleness is not bounded by anything in your system, since the source itself asserts changes retroactively.
- Follow that distinction to its uncomfortable conclusion — shortening the TTL to five minutes leaves the second unchanged. A zero-TTL cache still serves an answer that was false when computed, so anyone who proposes TTL tuning as the fix has misread which of the two is biting.
- Write the debt as a sentence with numbers: the service can answer 'covered' for a person whose coverage terminated up to the enrolment cadence plus its retroactive window earlier, with up to 24 hours of cache on top. That sentence is what a reviewer can accept or refuse; a ticket title is not.
- Name the mitigation that costs days rather than sprints: put service_date in the cache key and computed_as_of in the response body so consumers can see an answer's age, and re-check eligibility at claim submission rather than trusting the encounter-time answer.
- Give the measurement that sizes it in money — the weekly count and dollar value of claim_line rows denied for terminated coverage where the answer served at encounter time said active, filtered to the surviving claim version. That number is what buys the sprint later; without it the fix competes against features on opinion.
- Be specific about the acceptance. Name the artefact the decision lives in and the person with budget authority who signed it, because a compromise nobody accountable acknowledged is a decision you made alone and later described as a trade-off.
Follow-up
- The payer inquiry path ships and the cache stays. Which of the two staleness bounds has actually moved?
- A service was delivered against a stale 'covered' answer. Who absorbs the cost, and does your design record enough to answer that?
- Registration wants a 50ms p99 on this call. What do you give up, and how do you say that to them in one sentence?
- 01
Describe a time when you optimized a piece of code. What approach did you take?
- 02
What motivates you to work in the healthcare technology field?
- 03
You shipped the eligibility service against a fixed go-live with a 24-hour cache keyed on person and plan, no service date in the key and no computed-as-of field in the response, because the real-time payer inquiry path was two sprints behind. Enrolment files routinely terminate coverage retroactively — a file received on the fifth can terminate coverage effective the first. Describe shipping a compromise of this shape: what you wrote down, who accepted the risk, the bound you could honestly state, the measurement that sized the debt in money, and whether it was ever paid.
Is this an official Maven Clinic interview guide?
No. It is PracHub's own research and practice material for the Software Engineer role at Maven Clinic. Rounds and questions reflect what candidates have reported, not a process Maven Clinic has published, and they change over time. Confirm the current format and scope with your recruiter.
PracHub interview research ↗How difficult are the interviews, and how much preparation time is typical?
Interviews at Maven Clinic can be moderately challenging, especially in technical assessments. Candidates typically prepare for several weeks, focusing on relevant technologies and behavioral questions.
PracHub interview research ↗What differentiates successful candidates?
Successful candidates demonstrate both technical proficiency and cultural alignment with Maven's mission. They articulate their problem-solving processes effectively and show a genuine interest in women's health.
PracHub interview research ↗What is the culture like at Maven Clinic?
Maven Clinic fosters a collaborative and inclusive environment. The company values empathy and encourages team members to support one another while driving innovations in healthcare.
PracHub interview research ↗What is the typical timeline from the initial screen to an offer?
The interview process usually spans two to four weeks, depending on scheduling and responsiveness. Candidates are encouraged to follow up after interviews to stay informed.
PracHub interview research ↗Sources & methodology 3 sources ↗
Official role evidence, timestamped platform data and clearly labeled preparation advice.
- 01PracHub interview research ↗
PracHub editorial research into this company and role, maintained with this guide. Candidate-reported, not an employer publication.
platform · Accessed 2026-09-24 - 02PracHub Software Engineer practice ↗
Cross-company practice questions for this role.
platform · Accessed 2026-09-24 - 03PracHub interview preparation framework ↗
The framework the preparation plan follows.
platform · Accessed 2026-09-24
Explore related engineering environments and compare the preparation focus.
Garner health Software Engineer Interview Guide
Failure-handling practice for Garner health software engineer candidates: requests whose outcome a timeout leaves unknown, where a retry is safe, and deduplicating at the receiver instead of assuming exactly-once delivery.
Optum Health Services Software Engineer Interview Questions & Guide 2026
Caching exercises for Optum Health Services software engineer candidates: deciding what staleness a feature tolerates, choosing between expiry and explicit invalidation, and handling the stampede when a hot key expires.
Luminis Health Software Engineer Interview Questions & Guide 2026
Systems practice for Luminis Health software engineer candidates, weighted toward healthcare and life sciences work: the load that actually arrives, the failure modes that matter there, and the invariants a partial outage must not break.
SCA Health Software Engineer Interview Questions & Guide 2026
A study plan for SCA Health software engineer prep built on spaced review: commit to a full answer before opening any solution, and repeat every missed exercise three days later.