As a Software Engineer at Orca Bio, you are stepping into a mission-critical environment where precision, reliability, and innovation intersect. Orca Bio works in cell therapy, and your role is to ensure that the digital and technical infrastructure supporting the company's manufacturing and production processes is robust, scalable, and efficient.
You will work at the intersection of software development and complex biological manufacturing, contributing to systems that directly impact patient outcomes. This role is not just about writing code; it is about engineering solutions that maintain the integrity of life-saving therapies. You will collaborate with cross-functional teams, including Manufacturing Sciences and Technology (MSAT) and operations, to solve unique problems in a high-stakes, regulated environment.
Initial Screening
reportedThe person on this call usually cannot evaluate your code and does not need to. They write a short paragraph, and that paragraph is what a hiring manager skims when deciding who to put on your loop. So the test is not whether your work was hard, it is whether a non-engineer can repeat it correctly. Name systems by what they did rather than by their internal codename, give each project a shape (what was breaking, what you changed, what happened after), and keep the whole walkthrough near ninety seconds. Depth that cannot survive a paraphrase reads as vagueness.
What to demonstrate
- Whether a non-engineer can restate your projects without distorting them, since their paraphrase is what travels to the hiring manager, not your sentences
- Whether each project has a shape rather than a stack list: the failure or constraint, the change you made, the result and how it was measured
- Whether you can say what was yours inside a team project without either inflating it or disappearing into the plural
How to prepare
- Rewrite each headline project as two sentences with no internal system names and no acronyms outside your company, then say them to someone outside engineering and have them repeat them back. Fix whatever came back wrong
- Attach one measured number to each project: the baseline, the change, and the window it was measured over. Where nothing was ever measured, say that plainly rather than reaching for a plausible percentage
- Time the background walkthrough against a clock. If it runs past two minutes, compress the earliest role to a single clause and spend the recovered time on the most recent one
Technical Dives
reportedWhat this round decides is narrow: whether you can produce code that runs and is correct on inputs nobody showed you. An elegant solution that does not compile scores below a plain one that does, so write a correct brute force first, say out loud that you know its cost, and improve it with the working version still on screen. What separates strong answers is who finds the broken case. Trace your own code against an empty input, a single element, and duplicate keys before you say you are finished, because being told is far more expensive than noticing.
What to demonstrate
- Whether degenerate inputs get checked without being asked for: an empty collection, one element, every element equal, and the extreme value the input type allows
- Whether the complexity you state matches the code you actually wrote, including a sort or a copy sitting inside a loop
- Whether the finished answer is verified against the worked examples before you call it done, rather than assumed correct because the code reads correctly
How to prepare
- Take five problems you have already solved and, without running anything, write down what each returns for empty input, a single element, and all-duplicates. Then run them and count how many you predicted wrong.
- Drill the brute force as its own skill: on ten problems, write only the obviously-correct slow version and time how long it takes to get it passing. If that is more than a few minutes, that is what to practise, not the optimal version.
- Add a fixed last step before you submit anything, reading only the loop bounds and the initial value of each accumulator, which is where most off-by-one errors live
Team Meeting
reportedYou cannot drill a format you do not know, so put the preparation into material that travels. Three pieces of your own work, each rehearsed until you can take a follow-up you did not anticipate, will carry a conversation or a code walkthrough equally well. Specificity is what separates that from filler. A number needs its definition before it means anything: a p99 is over some window and measured at some hop, and a server-side figure excludes the queueing and network time a client would see. The number you cannot qualify is the one to leave out.
What to demonstrate
- Whether your examples carry detail only someone who did the work would hold, such as what the binding constraint actually was, which alternative you rejected and why it was worse, and what you measured on each side of the change
- Whether a number survives one follow-up, meaning you can say what it was measured over and whether it moved because of your change or merely alongside it
- Whether a failure is described with the specific change that followed it, rather than a lesson stated in general terms
- Whether your part in a team effort is stated accurately, including what other people did
How to prepare
- Write a page on each of three projects covering the constraint, the option you rejected, the measurement before and after, and what went wrong. Cut any line you cannot take a follow-up on, since you are writing the parts you will be pressed on rather than a summary.
- Recover the real figures while you still have access: request volume, data size, latency with its percentile and window, team size, timeline. Note where each came from, whether a dashboard, a design document or memory, and mark the estimates so you can say which they are out loud.
- Take your weakest project story to someone who works in a different area and have them ask why four times in succession. The point where you run out of answer is the part to go and re-read before the round.
PracHub editorial advice for the preparation topics above.
Storing monetary amounts as floating-point numbers.
Binary floating point cannot represent values like 0.10 exactly, so summing allowed and paid amounts across millions of claim lines accumulates representation and rounding error. Reconciliation against a remittance file is exact to the cent, so the drift surfaces as a balance that is off by a few cents with no identifiable cause, and engineers lose days looking for a logic bug in code that is correct apart from its numeric type. Integer minor units or an exact decimal type removes the class entirely.
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.
Optimising an axis nobody named
Ask which resource is actually scarce here: wall-clock latency, throughput, memory footprint, cost per request, or engineering time. Shaving a constant factor off an in-memory step is wasted effort when the same function makes a blocking remote call inside the loop.
Check-then-act on shared state
Read, decide, write is not safe under concurrency unless the decision and the write are one atomic step: a unique constraint with conflict handling, a compare-and-set, or a row lock held for the whole transaction. Two requests can both pass the existence check before either inserts, which shows up as duplicate rows under load and never in a single-threaded test.
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.
Worked solution 25 min
- Seed the heap with the head of each iterator and an empty emitted-id set.
- Pop the maximum by issued_ts, push that iterator's next head, and check the logical resource id against the set.
- On first sight record the id, then emit unless that version is entered_in_error; on a repeat sight suppress. Stop when emits reach the target.
- Return the rows plus a continuation token carrying the last (issued_ts, type, id) tie-break tuple.
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?
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.
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?
Resolve enterprise identity from a link log that supports unmerge
person_identity_link holds link_id, enterprise_person_id, assigning_authority, source_person_id, match_score, link_status (auto_linked, manual_linked, potential_duplicate, unlinked, rejected), version, superseded_by_link_id, decided_by, decided_at. You have up to 40 million source identities and 60 million decisions applied in decided_at order. Build a structure answering which enterprise identity a given (assigning_authority, source_person_id) resolves to after any prefix of the log, and supporting a revert of the k most recent merges at O(1) each. State the query complexity, and say why path compression is unavailable to you.
Approach
- Intern the node key as the pair (assigning_authority, source_person_id) into a dense integer index. Never key on source_person_id alone: two facilities in one network routinely issue the same medical record number to different people, so a bare-value key merges two patients before any matching logic has run, and a single-source test fixture will never show it.
- Classify the log rows before touching the structure. auto_linked and manual_linked are unions; potential_duplicate and rejected are recorded decisions that must not union anything; unlinked is a revert of the link it supersedes, not a new edge.
- Use union by size with an explicit undo stack, pushing (attached_root, its previous parent, the previous size of the absorbing root) on every union. Find walks parent pointers to the root: union by size bounds tree height at log2(n), so a query is O(log n) worst case, a union is O(log n), and a revert pops two words and is O(1).
- Path compression is the thing you have to give up, and the reason is specific: it rewrites parent pointers of nodes that were never named in the union being recorded, so the undo entry no longer describes the mutation that happened and a revert restores a forest that is quietly wrong. Near-constant find is only available to an append-only structure.
- Hold enterprise_person_id as a property of the root slot rather than a value copied onto every member. A merge then relabels one slot instead of n rows, and an unmerge restores two labels instead of reconstructing n.
- State the price plainly: every read path gains a resolution hop and no downstream table can carry a plain patient_id column. That cost is what buys reversibility, and it belongs in the design discussion rather than being discovered later by whoever writes the first join.
Follow-up
- A merge is found wrong three weeks and 200,000 unions later, and your stack only reverts a suffix. What do you do instead, and what does that cost?
- A claim was posted under the losing identity before the merge. After the unmerge, which identity owns it, and what in your design let you answer that?
- Two matcher instances submit unions concurrently. What is the smallest change that keeps both the structure and the undo stack consistent?
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?
How do you ensure your code is maintainable for other engineers on the…
How do you ensure your code is maintainable for other engineers on the team?
Approach
- State your assumptions explicitly before working the problem.
- Clarify what is being asked and what a complete answer contains.
- Say what you would check first and why it is the highest-information step.
Follow-up
- What assumption would you test first?
- How would you know your answer was wrong?
How do you ensure data integrity in a regulated, high-precision enviro…
How do you ensure data integrity in a regulated, high-precision environment?
Approach
- Clarify what is being asked and what a complete answer contains.
- Work from the requirement backwards to the design.
- State your assumptions explicitly before working the problem.
Follow-up
- How would you know your answer was wrong?
- What assumption would you test first?
Receive and verify asynchronous result notifications from a laboratory partner
A laboratory partner POSTs a notification to our webhook whenever an accession produces or corrects a result, and we then fetch the payload. Notifications arrive at least once, out of order, and are replayed in bulk when the partner restarts an interface. We must not act on a forged notification, must not process a replay as new work, and must not lose a notification whose accession we have never seen. Specify the endpoint contract: what is signed, what we verify, acknowledgement semantics, the replay window, and the consumer's behaviour for an unknown accession.
Approach
- Verify over the exact bytes received. HMAC-SHA256 over timestamp + '.' + raw request body with a shared secret, compared in constant time. Re-serialising the JSON before hashing breaks on key order, whitespace and unicode escaping, and a non-constant-time comparison leaks the expected signature byte by byte under timing.
- Bind the timestamp into the signed string and reject anything outside a narrow skew window such as 300s, so a captured notification cannot be replayed indefinitely. State the precondition honestly: the window is the partner's clock-skew budget, which is why it cannot be shrunk to nothing. Accept two secrets concurrently so rotation needs no coordinated cutover.
- Keep authentication and deduplication separate. A valid signature says the notification is genuine; it says nothing about whether we have processed it. Dedupe on UNIQUE (partner_id, notification_id) with INSERT ... ON CONFLICT DO NOTHING, the same atomic guard any at-least-once feed needs.
- Acknowledge fast, work later: verify, persist, return 200 inside the partner's timeout, and process asynchronously. Fetching and applying inline makes our processing latency their timeout and converts our slow dependency into their retry storm. Return 4xx only for what a retry cannot fix - 401 bad signature, 400 unparseable body - and 5xx for our own faults, because the partner's retry policy branches on exactly that line.
- Treat an unknown accession as ordinary traffic. Notifications routinely outrun the interface carrying the accession itself, so park the notification in a pending state keyed by accession and reconcile when it lands, with a stated maximum age that raises an alert. Rejecting it turns a recoverable ordering problem into permanent loss that nobody notices until a report is short.
Worked solution 30 min
- Write the signed-string construction and the header format, then the verification in order: parse header, check timestamp skew, compute the HMAC over raw bytes, constant-time compare against each accepted secret.
- Write the DDL for the received-notification table with UNIQUE (partner_id, notification_id) and a processing-state column.
- Write the status-code table - which failures are 400, 401, 409 and 5xx - and next to each, what the partner's retry policy does with it.
- Trace a bulk replay of 50,000 notifications and state where they are absorbed and the cost per notification.
- Write the pending path: where a notification for an unknown accession waits, what triggers reconciliation, and the age at which it alerts.
Follow-up
- The partner replays six hours of notifications after a restart. What in the design stops any of it being re-applied?
- How do you rotate the signing secret without a coordinated deploy on both sides?
- A correction and its original arrive in the wrong order. What decides which value a chart shows?
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?
Four days sample coding, design, fundamentals and the practical rounds at deliberately shallow depth, which is enough to surface the topics you did not know were in scope. That map, rather than a guess made on day one, decides where the last three days go.
Prepare, practise & reflect
One practical outcome each day. Spend longer where you need it.
0 / 7 done01Coding, one pass at shallow depth
- Solve one problem from each of six families, an array with two pointers, hash counting, binary search, a tree traversal, a graph traversal and one dynamic program, under a hard twenty-minute cap with no extensions, marking each finished, late, or stalled.
- For every stall, write the exact move you could not make rather than the subject, so the note reads could not turn the recurrence into a loop rather than bad at dynamic programming.
- Fix nothing today. The value of the pass is the unfixed record.
Deliverable: Six timed attempts marked finished, late or stalled, each stall carrying a named blocking move.
Practice prompt ↗Practice prompt ↗Worked solution ↗02Design, one pass at shallow depth
- Spend twenty minutes each on three different shapes, a read-heavy feed, a write-heavy ingest path, and something needing a transaction across two entities, stopping each at requirements, interface and data model.
- After each, write the first question you could not answer, which is usually a number you could not estimate or a failure mode you had no vocabulary for.
- Mark which of the three you would be most relieved not to be asked, and treat that as data rather than as a preference.
Deliverable: Three shallow designs, each with the first unanswerable question written at the bottom.
Practice prompt ↗Practice prompt ↗03Fundamentals and the practical rounds
- Answer eight short questions in writing at four minutes each, covering the material that fills the gaps between the big rounds: what happens between a URL and a rendered page, what an index costs on write, when a process is preferable to a thread, and what conditions a deadlock requires.
- Do one thirty-minute practical task of the kind a take-home compresses: read an unfamiliar two-hundred-line file and write what it does, what you would change, and the one thing you remain unsure of.
- Score every answer fluent, correct but slow, or absent, and keep the absent ones visible.
Deliverable: Eight scored short answers and one written reading of unfamiliar code.
Practice prompt ↗Practice prompt ↗04The rounds that are about you, and the map
- Deliver three behavioural answers aloud against a timer, a conflict, a failure you owned, and a decision made without enough information, marking any that ran past three minutes or contained no number.
- Assemble the map: every marked item from days one to three on a single page, sorted by how likely it is to appear in your loop rather than by how uncomfortable it felt.
- Choose exactly two areas for the remaining three days and write down what you are deliberately abandoning.
Deliverable: A one-page scored map of the whole surface area with two areas chosen and the rest explicitly abandoned.
Practice prompt ↗Practice prompt ↗Worked solution ↗05First chosen area, to the depth you skipped
- Work the higher-ranked area in four focused blocks, choosing items one level above where you stalled rather than repeating what already works.
- After each block write the rule you extracted in one sentence with its precondition attached, since a rule carrying no precondition is exactly what fails under a variation.
- Re-attempt the day-one or day-two item that exposed this area and compare against the original timing.
Deliverable: Four worked blocks, a timed re-attempt against the original, and three one-sentence rules with preconditions.
Practice prompt ↗Practice prompt ↗06Second chosen area, where the gap is coverage rather than speed
- Treat the second area differently from the first. Day five drilled something you could already half-do; this one is usually a topic you had simply never met, so build one worked reference example end to end and keep it, rather than attempting six problems badly.
- Write down the vocabulary you were missing on day two or three, five terms at most, each with the one sentence that makes it usable in an answer rather than the textbook definition.
- Redo the shallow attempt that exposed this area and note whether you now fail later in the problem, because moving the failure point is the realistic gain from a single day and is worth more than a score that did not change.
Deliverable: One worked reference example for the newly covered area, a five-term vocabulary list, and a note on where the failure point moved.
Practice prompt ↗Practice prompt ↗07Reassemble the loop
- Sit two rounds back to back with no gap, ordering them so the area you chose second comes last, because the map was built from rested, isolated attempts and the loop will reach your weaker area when you are already spent.
- Write where the second round suffered from the first, which is normally the point at which structure collapses into narration.
- Reduce the week to one page holding only the rules you can state without reading them.
Deliverable: Mock notes on cross-round carryover plus a one-page card of rules you can recite from memory.
Practice prompt ↗Worked solution ↗Expand any day for tasks and deliverables. Your progress is saved on this device.
Bring the two or three numbers the story rests on and know how they were collected. A p99 whose timer starts inside your handler excludes the time a request spent queued, so it can sit flat while users wait longer. Give the window, the percentile and what the measurement left out, or drop the number.
What is your experience with data visualization and reporting tools in…
What is your experience with data visualization and reporting tools in a technical workflow?
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.
Follow-up
- What would you do differently if you ran that again?
- How did you know your change caused the improvement?
Describe a time you had to troubleshoot a complex software issue in a …
Describe a time you had to troubleshoot a complex software issue in a production-critical system.
Approach
- Pick a story where you made the decision, not one where you watched it.
- Name the disagreement and how you resolved it with evidence.
- State the situation in two sentences and spend the rest on the reasoning.
Follow-up
- How did you know your change caused the improvement?
- What would you do differently if you ran that again?
How do you handle system downtime or unexpected technical failures dur…
How do you handle system downtime or unexpected technical failures during a critical operation?
Approach
- State the situation in two sentences and spend the rest on the reasoning.
- Close with what you would do differently, concretely.
- Pick a story where you made the decision, not one where you watched it.
Follow-up
- What did you decide not to do, and why?
- What would you do differently if you ran that again?
Describe a time you had to explain a complex technical concept to a no…
Describe a time you had to explain a complex technical concept to a non-technical stakeholder.
Approach
- 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.
- Pick a story where you made the decision, not one where you watched it.
Follow-up
- How did you know your change caused the improvement?
- What did you decide not to do, and why?
- 01
What is your experience with data visualization and reporting tools in a technical workflow?
- 02
Describe a time you had to troubleshoot a complex software issue in a production-critical system.
- 03
How do you handle system downtime or unexpected technical failures during a critical operation?
- 04
Describe a time you had to explain a complex technical concept to a non-technical stakeholder.
Is this an official Orca Bio interview guide?
No. It is PracHub's own research and practice material for the Software Engineer role at Orca Bio. Rounds and questions reflect what candidates have reported, not a process Orca Bio has published, and they change over time. Confirm the current format and scope with your recruiter.
PracHub interview research ↗How long does the interview process typically take?
The timeline can vary, but generally, it spans a few weeks from the initial screen to a final decision. The process is paced to give you enough time to meet the team.
PracHub interview research ↗What differentiates a successful candidate?
Successful candidates are those who show both deep technical curiosity and a genuine interest in Orca Bio's mission. They don't just solve the problem; they understand the context of the problem and how their solution helps the company.
PracHub interview research ↗Is there a specific coding language I should focus on?
Candidates report that being language-agnostic and able to pick up the tools needed for the job counts in their favor. However, experience with languages commonly used in data handling and automation is highly beneficial.
PracHub interview research ↗Will I be working remotely?
The nature of the role often requires proximity to Orca Bio's manufacturing sites to ensure effective collaboration. Check the specific location details for your role to understand the on-site requirements.
PracHub interview research ↗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.
- 01PracHub interview research ↗
PracHub editorial research into this company and role, maintained with this guide. Candidate-reported, not an employer publication.
PracHub page · Accessed 2026-09-22 - 02PracHub Software Engineer practice ↗
Cross-company practice questions for this role.
PracHub page · Accessed 2026-09-22 - 03PracHub interview preparation framework ↗
The framework the preparation plan follows.
PracHub page · Accessed 2026-09-22
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