As a Software Engineer at Avelios Medical, you are at the intersection of cutting-edge software development and high-stakes healthcare technology. The company focuses on digitizing complex medical workflows, meaning your code directly impacts the efficiency of clinical teams and, ultimately, patient outcomes. Whether you are building robust Full Stack features or architecting reliable Automated Software Testing frameworks, your work is fundamental to the stability and scalability of their medical platforms.
This role is not just about writing code; it is about solving intricate problems in a regulated, high-performance environment. You will be expected to balance rapid feature delivery with the rigorous quality standards required in the health-tech sector. If you are someone who thrives on building maintainable, high-impact systems and enjoys working in a collaborative, team-oriented culture in München, this position offers a unique opportunity to shape the future of digital health.
Preparation focus
editorialNo round sequence has been reported for this company, so work the categories below and confirm the format with your recruiter.
What to demonstrate
- Breadth across SQL, experimentation and product reasoning
- Ability to state assumptions before choosing a method
How to prepare
- Drill the practice exercises below and time yourself
- Prepare three quantified stories about decisions you drove
PracHub editorial advice for the preparation topics above.
Assuming admission, discharge and transfer messages arrive in the order the events happened.
Interface engines route by message type across separate queues and retry independently, so a discharge can land before the admission it closes and an update can land before the registration it modifies. Ordering has to come from the sender's event timestamp plus a per-encounter sequence, and the consumer has to apply out-of-order and late-arriving events correctly rather than rejecting them, because rejection turns a recoverable ordering issue into permanent data loss that nobody notices until a report is short.
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.
Finishing a solution without stating its complexity
Give time and space in the same breath as the code, and define n explicitly when there are two sizes, since n nodes and m edges are not interchangeable. Space is the half that gets skipped: count the auxiliary structures you allocate and the recursion stack at its deepest, not only the answer you hand back.
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.
Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.
Merge twelve resource streams into one patient summary page
A patient summary fans out to between 8 and 12 resource types. Each returns a network-backed, paged iterator of resource versions sorted by issued_ts descending, up to 200,000 versions per type for one person. Return the 50 most recent current versions across all types, where current means no later version supersedes it within the same logical resource, and a logical resource whose current version is entered_in_error is omitted entirely. You may not materialise the iterators. Give time and space in terms of k types, the result size and the page size.
Approach
- k-way merge with a max-heap holding one head per iterator, keyed on issued_ts. Seeding is O(k), each pop is O(log k), so reaching R emitted rows costs O(k + P log k) for P pops, with O(k) heap space plus one page buffered per iterator. Fetching everything and sorting is O(V log V) over V up to 2.4 million versions and drags every page across the network to produce 50 rows.
- Suppress with a hash set of logical resource ids already seen, recorded on first sight whether or not that version is emitted. A logical resource has exactly one resource type, so all of its versions arrive on one iterator, and that iterator is descending in issued_ts: the first version you see for a logical resource is its newest. Deciding on first sight and suppressing every later pop for that id is therefore correct in one pass with no lookahead.
- Count emits, not pops. A correction-heavy chart can burn many pops per emitted row, so a loop that stops at 50 pops returns a short page. Put a bound on total pops as well, and when it trips, return what you have with a continuation token rather than spinning.
- Break issued_ts ties deterministically on (resource type, resource id). Without it two identical requests return two different orderings and the next page silently skips or repeats rows.
- Handle entered_in_error at first sight: the erroneous version still supersedes its predecessor, so record the id in the seen set and emit nothing, dropping the whole logical resource instead of falling back to the value it replaced. Recording it is the load-bearing half. Skip it and the next pop re-displays the value a clinician already retracted.
- Summarise the budget honestly: the composite p99 is what the user feels, and it is bounded below by the slowest of the k iterators, so the merge fixes the ordering cost but not the fan-out tail.
Follow-up
- One of the twelve iterators has a p99 of 400ms while the rest return in 20ms. What is your composite p99 and what would you change first?
- The user pages to rows 51 through 100. How do you resume without re-reading from the top, and what breaks if issued_ts is not unique?
- One resource type is accidentally returning ascending order. How would your code detect that rather than quietly emitting the oldest rows?
Reconstruct what the chart displayed at five million past instants
observation_result holds 200 million versions: observation_id, enterprise_person_id, filler_order_id, loinc_code, value_numeric, result_status, collected_ts, issued_ts, version, supersedes_observation_id. An incident review hands you 5 million queries of (enterprise_person_id, filler_order_id, loinc_code, as_of_instant). For each, return the version that was visible at that instant, meaning the one with the greatest issued_ts at or before it. Neither side fits in memory. The per-query scan is correct. Explain precisely why it is too slow, then give a plan with its complexity.
Approach
- Name the clock before naming an algorithm. Visibility is issued_ts, the release time. collected_ts is when the specimen was drawn and can precede release by hours, so ordering on it reports a correction as visible long before anyone could have seen it. No amount of index work rescues the wrong column.
- Be exact about why the naive plan fails, because the interviewer is testing whether you can tell arithmetic cost from I/O cost. Per query the chain scan is O(V_k) and the arithmetic is trivial, but 5 million independent lookups into a 200-million-row structure that does not fit in RAM is 5 million random reads. The job is bounded by seeks per query, not by comparisons, and buying a faster comparison changes nothing.
- Convert random access into sequential access. Hash-partition both sides on the chain key (enterprise_person_id, filler_order_id, loinc_code) into P shards sized to fit memory, sort each shard's versions by (chain key, issued_ts) and its queries by (chain key, as_of_instant), and sweep the pair in lockstep. Total O((V + Q) log(V + Q)) with external sort, replacing Q seeks with two sequential passes.
- Inside a chain the sweep is linear, not logarithmic, because queries are visited in ascending as_of order and the version pointer only moves forward: O(V_k + Q_k) per chain. Binary search per query is the better shape only when Q is small relative to V and the versions are already indexed and resident.
- Return the empty answer as a distinct outcome. A query whose as_of precedes the first issued_ts means nothing was displayed, which is not the same as the earliest value, and is frequently the exact fact the review is chasing.
- Return a version later marked entered_in_error if it was live at the instant asked about. Reconstructing the past means reporting what was on the screen, including what was wrong, and quietly substituting today's truth defeats the purpose of the exercise.
Follow-up
- Read replicas lagged 40 seconds at the time. Does your answer describe what the clinician actually saw, and how would you bound the difference?
- Serve the same question online for a single chart at a p99 under 50ms. What changes?
- One partner changed its filler_order_id format mid-year, so the chain key is not stable. How does that appear in your output, and how do you detect it rather than returning empty answers?
Flag a requester reading too many distinct charts per window
You consume access-audit records (event_ts, requester_id, enterprise_person_id, purpose_of_use, break_glass) at tens of thousands per second, non-decreasing in event_ts. For each requester, emit an alert the first time any sliding 600-second window contains reads of more than D distinct enterprise_person_ids. Break-glass reads count toward the window and are also reported separately. Return (requester_id, window_start_ts, distinct_count). Target O(1) amortised per event and memory proportional to the events resident in the window, not to the day.
Approach
- Per requester, hold a deque of (event_ts, person_id) and a hash map from person_id to its occurrence count inside the window, plus a running distinct counter. Push on the right; while the front is older than event_ts minus 600 seconds, pop it, decrement its count and erase the key when the count reaches zero, decrementing the distinct counter. Each event is pushed once and popped once, so the amortised cost is O(1) and the memory is O(W) for window occupancy W.
- Test the threshold immediately after each push and nowhere else. Between two consecutive events the window can only lose members as its left edge advances, so the maximum distinct count over all window positions is attained at a position whose right edge is an event. Checking at pushes is therefore exhaustive rather than a sampling approximation.
- Latch the alert per requester and re-arm only when the distinct count falls back below D, otherwise one busy stretch emits thousands of near-identical rows and the real signal is buried by its own volume.
- Bound memory both per requester and globally. A requester whose window legitimately holds tens of thousands of reads must not hold the process hostage, so cap the deque and degrade above the cap to an approximate distinct counter such as HyperLogLog, stating the error you accept in exchange.
- Count break-glass toward the window but carry it in its own output field. Break-glass has to succeed during an emergency, which is exactly why it must be the most visible path in the audit, and excluding it from the count would make the abuse route the quiet one.
- State the precondition: this is correct only while input is non-decreasing in event_ts. Out-of-order arrival needs a bounded-lateness buffer and a watermark, and dropping late events without a counter is the failure that hides itself.
Worked solution 25 min
- Implement push, then the eviction loop, then the distinct counter update, in that order, and assert the counter against the map size after each event.
- Set D to 25 and feed 15 distinct persons between 09:06:00 and 09:08:59.
- Feed 15 more distinct persons from 09:11:00, one every eight seconds.
- Record the event at which the alert fires and the window_start it reports.
- Re-run the same events through fixed ten-minute tumbling buckets and compare.
Follow-up
- One region's events arrive up to 90 seconds late. What buffer do you add, and what does that do to alert latency?
- One person uses two requester accounts. What has to change in the key, and what new false positive does that introduce?
- How do you restore window state after a process restart without replaying the whole day?
Reproduce and fix a lost update on a deductible accumulator
An accumulator row holds deductible_applied_cents bigint and plan_deductible_cents bigint, keyed by (enterprise_person_id, plan_id, benefit_year). The adjudicator opens a transaction, SELECTs the row, computes the member's share in application code, then UPDATEs the row to the absolute new total it computed, all under PostgreSQL's default READ COMMITTED. Two claim lines for one member adjudicate concurrently: both charge the member deductible, but the accumulator advances by only one of the two amounts, so the member is billed deductible again on a later line after the plan deductible has already been met. Write the exact two-session interleaving that produces it. Then give three fixes, each naming the lock or isolation level, the SQLSTATE you must handle, and the throughput cost.
Approach
- State the anomaly precisely. READ COMMITTED takes a fresh snapshot per statement, so it prevents dirty reads but permits a lost update when a transaction reads a value, computes outside the database, and writes back an absolute result. The vulnerable window is the round trip through application code between two statements, not the transaction boundary — the same logic expressed as one relative UPDATE is safe at this very isolation level.
- Write the interleaving as an ordered script both sessions can be replayed from, with the commit points marked, and make both writes absolute (SET deductible_applied_cents = :computed_total). The point is not that two writes happen — it is that the second write stores a total computed from a value that had already been superseded by the time it landed.
- Fix one, SELECT ... FOR UPDATE on the read: the second session blocks on the row lock and, under READ COMMITTED, re-reads the newest committed version when it unblocks, so its computation starts from the winner's total. No serialization error to handle. Cost is serialised throughput per member and a lock held for the transaction's whole duration, so nothing inside may call an external payer.
- Fix two, REPEATABLE READ or SERIALIZABLE with a retry loop: in PostgreSQL, REPEATABLE READ aborts the second writer of a row with SQLSTATE 40001, 'could not serialize access due to concurrent update'; SERIALIZABLE additionally aborts on read/write dependencies detected by SSI, also under 40001. The retry re-runs the whole transaction, so the claim application must be idempotent or keyed by claim_line_id, or a retry double-applies.
- Fix three, single-writer partitioning: route by hash(enterprise_person_id) to one consumer per partition, so no two transactions ever touch one accumulator. No locks, no retries; the cost is head-of-line blocking behind a slow claim, rebalancing during deploys, and losing the ability to scale a hot member.
- Name the cheapest option and its real catch. A single UPDATE ... SET deductible_applied_cents = LEAST(plan_deductible_cents, deductible_applied_cents + :amt) ... RETURNING deductible_applied_cents does not lose the update: under READ COMMITTED an UPDATE that hits a concurrently updated row blocks, then re-evaluates its expressions against the newly committed version, so both increments land. The catch is that the member's share is the delta this statement actually applied, which is the returned value minus the row's pre-image — and RETURNING does not expose the pre-image before PostgreSQL 18's OLD/NEW aliases. On earlier versions you must read that pre-image under the same row lock, which puts you back in fix one with a shorter critical section. The one-statement form is a clean fix only when the caller does not need the delta.
Follow-up
- A claim is reversed six months later under a plan design that has since changed. What amount does the reversal subtract, and where is that number stored?
- Your retry loop hits 40001 repeatedly for one member during a batch window. What is the backoff, and at what point do you stop retrying and pend the claim?
- Which of the three fixes survives a retroactive eligibility change that invalidates everything applied in the last month, and what does the rebuild look like?
Denormalise consent into a decision table with a stated staleness bound
Access governance answers 'may this requester read this person's data for this purpose' on every identified read — tens of thousands per second at a 5ms p99 — from consent_directive: consent_id, enterprise_person_id, version, scope, purpose_of_use text[], grantee_org_id (NULL meaning all organisations), permit boolean, effective_ts, expires_ts, revoked_ts, status. Evaluating the normalised version history per read misses the budget. Design the denormalised decision store and its key, state the revocation staleness bound in seconds and the mechanism that actually enforces it, and say which normalised rows remain the system of record and why.
Approach
- Derive the shape from the read, not from the source table. The read is a point lookup on (person, grantee organisation, purpose), so fan the purpose_of_use array out into one row per purpose and make those three columns the primary key. A GIN index on the array would serve containment queries nobody issues.
- Handle the wildcard concretely. grantee_org_id NULL means 'all organisations', and NULL never equals anything, so a nullable column in the key breaks both the primary key and the equality lookup. Store a sentinel org id of 0 for the wildcard and resolve specific-before-wildcard in the lookup, or the wildcard row is silently unreachable.
- Make refusal a row, not an absence. An explicit permit = false must outrank a permit for the same key, so carry a precedence rank (specific grantee beats wildcard, refusal beats permit at equal specificity) and resolve with ORDER BY rank LIMIT 1. Default deny when nothing matches, so a rebuild failure fails closed.
- Separate the two staleness mechanisms and be precise about which one is the bound. The in-process decision cache has a TTL; a revocation also publishes an invalidation. The publish shortens the typical case to milliseconds, but it can be lost, so the guaranteed bound is the TTL alone — quote that number, for example 30 seconds, and justify it against the cache hit rate you need to hold 5ms p99.
- Evaluate expires_ts at read time against the request clock rather than baking the decision. A cached permit whose expires_ts has passed is wrong regardless of invalidation, because no event fires when a timestamp simply goes by.
- Keep consent_directive as the system of record: audit must reconstruct the directive in force at any past instant, the decision table holds only current state, and a corrupted decision table must be rebuildable by replaying the directives. Bound the write amplification by keeping grantees at organisation granularity and purposes a closed code set, so one directive expands to a known small number of rows rather than an unbounded cross product.
Worked solution 40 min
- Write the decision table DDL with the three-column primary key, the sentinel wildcard, the precedence rank and a monotonic decision_version.
- Write the lookup query: equality on the key with the wildcard row unioned in, ORDER BY rank, LIMIT 1, and an expires_ts predicate evaluated against the request timestamp.
- Write the projection that turns one consent_directive version into its decision rows, and compute the expansion factor for a directive covering four purposes and one organisation.
- State the TTL, then justify it: at N reads per second and M people, compute the cache hit rate the TTL yields and check it against the 5ms p99 budget.
- Write the failure narrative for a lost invalidation and confirm the TTL, not the bus, is what caps exposure.
- Write the rebuild procedure from consent_directive and the assertion that proves the rebuild is complete before it is swapped in.
Follow-up
- Break-glass must always succeed and must be unmistakably marked. Where does it sit relative to this lookup, and what stops it from becoming the quiet default path?
- The invalidation bus is down for thirty minutes. Walk through what a revoked person's data exposure looks like minute by minute, and what the backlog does when the bus returns.
- Where does the access audit record get written so that an audit-store outage degrades neither the log's completeness nor the read's availability?
What are the key differences between various front-end frameworks when…
What are the key differences between various front-end frameworks when handling real-time data?
Approach
- Clarify what is being asked and what a complete answer contains.
- Work from the requirement backwards to the design.
- Say what you would check first and why it is the highest-information step.
Follow-up
- How would you know your answer was wrong?
- What assumption would you test first?
Describe your process for identifying and resolving performance bottle…
Describe your process for identifying and resolving performance bottlenecks in a web application.
Approach
- State your assumptions explicitly before working the problem.
- 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.
Follow-up
- What assumption would you test first?
- How would you know your answer was wrong?
How do you ensure high code quality in a large-scale Full Stack applic…
How do you ensure high code quality in a large-scale Full Stack application?
Approach
- Work from the requirement backwards to the design.
- 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.
Follow-up
- What assumption would you test first?
- How would you know your answer was wrong?
Evolve a coverage contract to bitemporal without breaking shipped clients
GET /coverage/{person_id} has for years returned a flat list of {payer_id, plan_id, effective_date, termination_date, status}, where a null termination_date means open-ended. Callers now need to ask coverage questions as of a past service date, and to see what the system believed at an earlier instant, because retroactive terminations are routine traffic. Dozens of deployed clients cannot be upgraded on our schedule and some reject unknown fields. Design the evolution: the new shape, the versioning mechanism, the default behaviour for an old client, and the deprecation path with the checkpoints that gate it.
Approach
- Classify the change before choosing a mechanism. Adding a field is safe only under two preconditions: no client validates against a closed schema, and no client reads the object and writes it back, because a round-trip silently drops fields the client does not model. The prompt states some clients reject unknown fields, so 'just add as_of' is not additive here - that is the whole difficulty.
- Separate the two time axes the new shape must expose, since conflating them is the usual bug. Business time answers which coverage period applies to a service date; system time answers what we believed at an instant. The new resource takes both - an as_of date and an asserted_at instant - and the old response is exactly the projection as_of = today, asserted_at = now.
- Choose the version carrier and defend it. A new path is blunt but unmistakable and caches cleanly; a media-type parameter negotiates per request but is ignored by clients that do not set Accept and by any cache not keyed with Vary; a query parameter is trivial to set and pollutes cache keys. A new resource path for the shape change plus strictly additive evolution within each version is the defensible combination, and under any choice the old URL must keep returning the old bytes, null-means-open-ended included.
- Never widen an existing field's meaning. If 'termination unknown' now needs representing, it cannot be null, because every deployed client already reads null as open-ended; it needs a new, explicitly enumerated field present only in the new shape. Redefining a value in place is the change that alters every client's behaviour without a single one being redeployed.
- Run the deprecation on evidence rather than a calendar. Instrument per-client usage of the old resource, serve Deprecation and Sunset headers with a Link to the successor, and gate each checkpoint - announce, stop new clients, contact the remaining callers by name, freeze, remove - on that metric. State what an unmigrated client gets at sunset: an explicit, documented error, never a silently different shape.
Worked solution 40 min
- Write the old response and the new response side by side and mark every field that was added, removed, or had its meaning changed.
- For each marked field write the concrete failure a deployed client suffers - parse error, wrong decision, field dropped on round-trip - and the client behaviour that is its precondition.
- Write the projection rule that derives the old shape from the bitemporal store, then test it on a person with a retroactive termination and confirm the old bytes are unchanged.
- Choose the version carrier, write request and response for both resources, and state each one's cache key including both time axes.
- Write the deprecation timeline with its headers, the per-client usage metric gating each checkpoint, and the response an unmigrated client receives at sunset.
Follow-up
- An old client passes a filter parameter you only added in the new version. What do you return?
- A caching layer sits in front of both resources. What must be in the cache key for an as-of read, and what breaks if it is not?
- How would you prove no client round-trips the object back to you, before you rely on additive evolution?
Duplicate results appear only in the hour after interface restarts
About one in 40,000 ingested results produces a duplicate observation_result row. It happens only in the hour after an interface engine restarts and never reproduces in tests. The consumer checks a ledger table for the idempotency key, then writes the observation, then inserts the ledger row in a second transaction. The ledger has a unique index on the key, and the losing insert logs duplicate, ignoring. Adding a debug log between the check and the write made duplicates more frequent. Diagnose and fix.
Approach
- Run the query that splits the hypotheses before anything else: for one duplicate pair, compare their idempotency keys. Identical keys mean the key is right and mutual exclusion is broken. Different keys mean the key itself is wrong — a sender reusing or rotating control identifiers — which is a different bug with a different fix. Most time lost on this class of problem is lost by skipping this step.
- Measure the gap between the pair. created_at deltas in the tens of milliseconds, attributed to two different worker identifiers, mean two workers processed the same replayed message concurrently. Deltas of minutes or hours mean the ledger row was never durably written — rolled back, or written in a transaction that later aborted — and a later replay legitimately re-processed the message.
- Read the transaction boundaries rather than the isolation level. The check, the clinical write and the ledger insert span two transactions, so the window between the check and the ledger insert is unprotected at every isolation level, READ COMMITTED and SERIALIZABLE alike. The unique index protected the ledger; it never protected the observation, which had already committed by the time the conflict was detected.
- Explain why instrumentation moved it. The debug log widened the window between the check and the ledger insert, so more concurrent pairs landed inside it. Deleting the log narrows the window and hides the defect without fixing it — the same reason a single-threaded test suite is green, and the reason the incident correlates with restarts, when the broker redelivers a window of messages at once.
- Fix by turning check-then-act into one atomic conditional write: in a single transaction, INSERT INTO ingest_ledger (idem_key, ...) VALUES (...) ON CONFLICT (idem_key) DO NOTHING RETURNING idem_key, and if no row comes back, do nothing further. The observation insert belongs in that same transaction. Under READ COMMITTED the second inserter blocks on the unique index until the first commits and then sees the conflict, so the index — not the isolation level — supplies the mutual exclusion.
- Prove it with a deliberate concurrency test: two sessions released by a barrier on the same key, several thousand rounds, asserting exactly one observation row each time, then replay a captured restart window and diff row counts against a clean run.
Follow-up
- The clinical write must go to a different store than the ledger. How do you keep this atomic without a distributed transaction, and what does the outbox look like?
- The sender's control ID counter rolls over and begins reusing values. What breaks in your key, and what production signal would tell you it happened rather than a customer telling you?
Day one measures instead of guessing, under a fixed rubric, and the remaining hours are allocated in proportion to the gaps before any studying begins. The allocation is deliberately not renegotiated midweek, because the area that feels worst on day three is usually the one that is moving.
Prepare, practise & reflect
One practical outcome each day. Spend longer where you need it.
0 / 7 done01Diagnostic, scored before you study anything
- Sit a 110-minute diagnostic in four blocks: forty-five minutes on two coding problems, twenty-five on one design prompt taken to interface and data model, twenty of short-answer fundamentals, and twenty delivering two behavioural answers aloud.
- Score each block from 0 to 3 on a fixed rubric where 3 is correct and fluent, 2 is correct but slow or prompted, 1 is partially correct and 0 is stuck, grading the artifact rather than how the attempt felt.
- Allocate days two to five in proportion to 3 minus each block's score, write the allocation down, and commit to leaving it alone.
Deliverable: A scored rubric and a fixed hour allocation for the rest of the week.
Practice prompt ↗Practice prompt ↗Worked solution ↗02Largest gap: find the boundary rather than the subject
- Split the weakest area into named sub-skills and rate each separately. For coding those are restating the problem, choosing the structure, stating the invariant, turning the invariant into loop bounds, handling empty and single-element input, and accounting for complexity out loud.
- Attempt three items positioned just above where the rating drops off, and for each write the first move you failed to make.
- Re-attempt one of them from blank four hours later with nothing open.
Deliverable: A sub-skill map with the two blocking sub-skills circled.
Practice prompt ↗Practice prompt ↗03Drill the blocking sub-skill by repeating the shape
- Do eight short repetitions of the same shape rather than eight different problems, so what gets practised is the pattern and not the puzzle.
- State the rule you now hold in one sentence, then test it against a case built to break it, a sliding window over an array containing negative values, or a cache-aside read path whose invalidation message is dropped.
- Have someone else read your one-sentence rule and find the precondition you left out.
Deliverable: One rule statement with its preconditions attached and one counterexample that would have caught the incomplete version.
Practice prompt ↗Practice prompt ↗04Second gap, plus maintenance on the strongest area
- Run the same sub-skill decomposition on the second-largest gap in half the time.
- Spend twenty-five timed minutes on the block you scored highest, choosing the hardest item you can still finish rather than a warm-up.
- Write whether each area fails you on recall, on setup, or on execution, and set the fix accordingly: repetition for recall, a written checklist for setup, timed work for execution.
Deliverable: A second sub-skill map plus a one-line failure diagnosis for each area.
Practice prompt ↗Practice prompt ↗Worked solution ↗05The gap that is not a skill
- Record one technical and one behavioural answer, then count two things in the playback: seconds before your first clarifying question, and sentences you began without knowing where they would end.
- Practise saying that you do not know, followed by how you would find out, without letting it soften into a guess, and practise stating a complexity or an estimate before being asked for it.
- Redeliver one answer under a hard ninety-second cap, which forces structure ahead of detail.
Deliverable: Two recordings with a counted reduction in time-to-first-question.
Practice prompt ↗Practice prompt ↗06Retest under day-one conditions
- Sit the same 110-minute structure with new prompts of comparable difficulty and score it on the identical rubric.
- For any block that did not move, change the method rather than adding hours: a block stuck at 1 usually means the practice was too varied, not too short.
- Write down which single block you would still lose the offer on.
Deliverable: A second scored rubric placed beside the first, with one named remaining risk.
Practice prompt ↗Practice prompt ↗07Full loop under interview conditions
- Run a sixty-minute mock over the two blocks that moved least, with an interviewer briefed to interrupt and change direction mid-answer.
- Write the recovery script for going blank: restate the question, state your assumption, name the first thing you would check.
- Say every rule from the week aloud without reading it, and cut any you cannot state in a single sentence, since a rule you have to reconstruct mid-answer will not survive an interruption.
Deliverable: A one-page card holding the recovery script and only the rules you could state from memory.
Practice prompt ↗Worked solution ↗Expand any day for tasks and deliverables. Your progress is saved on this device.
Counting review comments or mentees proves nothing. The useful version is a specific change you approved with a reservation you stated, or one you blocked and the delay that cost. Say which standard you were holding and why it was worth the friction. A mentoring story needs the thing the other person can now do without you.
How do you handle concurrency issues in a multi-user clinical software…
How do you handle concurrency issues in a multi-user clinical software environment?
Approach
- State the situation in two sentences and spend the rest on the reasoning.
- 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 would you do differently if you ran that again?
- What did you decide not to do, and why?
Argue against rewriting foreign keys during a person merge
A proposal on your team simplifies merges: on merge, UPDATE enterprise_person_id across encounter, observation_result and claim_line to the surviving identity, then delete the link-resolution step so a patient-scoped read becomes one equality predicate. The author brings a p99 improvement on the patient summary, which fans out across eight to twelve resource types. You argued against it. Reconstruct the argument: the failure you predicted, the evidence you brought, the cost of your own alternative that you conceded, and who made the call.
Approach
- The probe is whether you can oppose a real performance win without retreating to data-hygiene arguments. Name the operation the proposal removes: unmerge. Once enterprise_person_id is rewritten in place, the row no longer records which source identity asserted the fact, and the only pair of columns that could — assigning_authority and source_person_id — lives in the link table the proposal deletes.
- Make it concrete with money rather than with charts. A claim_line posted under the losing identity carries coverage_id pointing at that identity's enrolment span. Rewriting the person while leaving coverage_id yields a row asserting the surviving person was covered under a span that was never theirs, which is a financial statement that adjudication and reconciliation will both act on.
- Do not argue that merges are rare, because that concedes the premise. Bring the rate of merges, the rejection rate on manually reviewed links, and the lag between a wrong merge and its discovery — false merges are a property of probabilistic matching, so the design has to assume them rather than hope against them.
- Concede the read cost honestly, since that is where the proposal's benefit is. Resolution through a versioned link table turns one equality predicate into a lookup feeding a predicate over a set of source identities, which changes index shape and raises the composite read's p99, and the composite is the number that matters, not the per-resource one.
- Offer the option that captures most of the win: a materialised current-link projection refreshed inside the merge transaction, so reads stay one predicate while the versioned link table remains authoritative and unmerge stays a supported operation rather than a recovery exercise.
- Finish on the decision process, not the technical point: what you wrote down, whether you blocked the change, who owned the call, and what you did once it was made — including if it went against you.
Follow-up
- Your projection and the link table disagree after a failed merge retry. Which one is authoritative, and how does a reader find out?
- An unmerge lands eight months later. What has to happen to the claims adjudicated under the merged identity in between?
- The author says reversibility can be handled by a nightly backup restore. Take that seriously and say exactly what it does not recover.
Own an incident where the matcher linked two different people
You shipped the change that moved the identity matcher's auto-link threshold from 0.94 to 0.88, to cut the manual review queue. Six hours later a clinician reports another person's results on a chart. person_identity_link has roughly 4,000 auto_linked rows since the deploy, and the longitudinal record service resolves patient reads through that table, so every bad link is already widening chart reads. Describe an incident of this shape that you owned: detection, what you stopped first, how you reversed links that must stay reversible, and the durable change afterwards.
Approach
- The probe is whether you measure blast radius in the units the domain cares about. Open with the number of auto_linked rows written since the deploy, how many of those join two source records that disagree on a demographic the matcher did not weigh, and how many patient reads resolved through them — not with the root cause, which hides whether you could see the problem at all.
- Be honest that the false-link rate is not a count you can query. There is no ground-truth column saying two source records are the same human, so the first defensible number is precision on an adjudicated sample with a stated sample size, and everything downstream of it is an estimate.
- Separate stopping from fixing, and name both stop actions. Reverting the threshold halts new bad links within a deploy cycle and does nothing to the ones already written; any cached patient summary or materialised projection keyed on enterprise_person_id keeps serving the merged chart until it is invalidated.
- Say what reversal costs on this schema: new person_identity_link rows at version+1 with link_status 'unlinked', and superseded_by_link_id set on the bad rows. No DELETE, because the question an incident review asks later is what the index believed at a specific minute, not what it believes now.
- Escalate clinical exposure rather than data exposure. Enumerate the encounters and orders placed during the window against the affected enterprise ids, because someone may have acted on another person's result; that list, not the row count, is what goes to safety review.
- Close on what made the threshold reviewable afterwards: a shadow run scoring the candidate threshold against live traffic without writing links, a precision target measured on a labelled set, and an alert on auto-link rate per hour so the next one is caught by a metric rather than by a clinician.
Follow-up
- Two of the bad links were adjudicated and confirmed wrong, but a claim was already submitted under the merged identity. What do you do with that claim, and who decides?
- Your shadow run shows the old threshold also produces false links, at a lower rate. Does that change whether this was an incident?
- How would you have detected this in fifteen minutes instead of six hours, and what would that alert cost you in false positives during a normal registration peak?
- 01
How do you handle concurrency issues in a multi-user clinical software environment?
- 02
A proposal on your team simplifies merges: on merge, UPDATE enterprise_person_id across encounter, observation_result and claim_line to the surviving identity, then delete the link-resolution step so a patient-scoped read becomes one equality predicate. The author brings a p99 improvement on the patient summary, which fans out across eight to twelve resource types. You argued against it. Reconstruct the argument: the failure you predicted, the evidence you brought, the cost of your own alternative that you conceded, and who made the call.
- 03
You shipped the change that moved the identity matcher's auto-link threshold from 0.94 to 0.88, to cut the manual review queue. Six hours later a clinician reports another person's results on a chart. person_identity_link has roughly 4,000 auto_linked rows since the deploy, and the longitudinal record service resolves patient reads through that table, so every bad link is already widening chart reads. Describe an incident of this shape that you owned: detection, what you stopped first, how you reversed links that must stay reversible, and the durable change afterwards.
Is this an official Avelios Medical interview guide?
No. It is PracHub's own research and practice material for the Software Engineer role at Avelios Medical. Rounds and questions reflect what candidates have reported, not a process Avelios Medical has published, and they change over time. Confirm the current format and scope with your recruiter.
PracHub interview research ↗How difficult are the technical interviews?
They are challenging but fair, focusing on real-world application rather than abstract puzzles. Prepare by brushing up on your core stack and practicing common architectural patterns.
PracHub interview research ↗What is the culture like at Avelios Medical?
The culture is collaborative, intellectual, and mission-driven. You will find a team that values precision, quality, and a shared goal of improving medical workflows.
PracHub interview research ↗How long does the process take?
While timelines vary by candidate and team, the process is structured to move efficiently once you have successfully cleared the initial technical screens.
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 2026-09-22 - 02PracHub Software Engineer practice ↗
Cross-company practice questions for this role.
platform · Accessed 2026-09-22 - 03PracHub interview preparation framework ↗
The framework the preparation plan follows.
platform · Accessed 2026-09-22
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