As a Software Engineer at Loop Health, you are at the forefront of building technology that simplifies the complex world of healthcare. Your work directly impacts how users interact with medical services, insurance, and wellness benefits. You are expected to design and implement scalable solutions that handle critical, real-time data, ensuring that the platform remains reliable and user-friendly for those managing their health.
The role is intellectually demanding and requires a blend of rigorous technical problem-solving and a deep understanding of system architecture. You will work within a fast-paced environment where your contributions are measured by your ability to translate ambiguous requirements into clean, maintainable code. Whether you are optimizing data structures for high-frequency access or designing robust system components, your impact is felt in the stability and efficiency of the Loop Health ecosystem.
Initial Screening
reportedThe title covers product work, platform work, infrastructure, mobile and frontend, and those are different jobs with different loops behind them. A screening call is the cheapest place to find out which one the seat is, and asking reads as experienced rather than fussy. The questions that separate them: what the team is on call for, what the last three projects were, and whether any round happens inside an existing repository instead of a blank file. Then say which of that you have done and which you have not. Claiming the whole posting is the fastest way to be found out one round later.
What to demonstrate
- Whether you can locate your experience inside one flavour of the role honestly instead of claiming the entire requirements list
- Whether you name what you have not done, which an experienced screener reads as a level signal and can plan the loop around
- Whether what you want next matches what the seat is: someone who wants greenfield work landing on a team that mostly operates an existing system is a hire that leaves within the year
How to prepare
- Mark every line of the posting as done, adjacent or new, and write one sentence for each adjacent line naming the closest thing you actually built
- Split your last two years into rough percentages across feature work, operating and debugging live systems, and design or review, so a question about scope gets numbers rather than adjectives
- Bring three questions that discriminate between seats: what the team is paged for, how much of the work is changing existing code versus standing up something new, and what shipped in the last quarter
Technical Rounds
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
Design Challenges
reportedBecause the format is not fixed, the first job in the room is classification. Listen to the opening question and decide what it is: a probe into work you have already described, a fresh problem to solve now, or a conversation about how you operate. Each wants a different register, and the common failure is forcing a rehearsed structure onto a question that did not ask for it. Running a full design ritual on a ten-minute debugging question reads as not listening. When you cannot tell which it is, ask how long they want to spend and answer at that depth.
What to demonstrate
- Whether the shape of your answer matches the question, so a yes-or-no gets answered before it is justified and an open prompt gets a direction before a detour
- Whether you check how much depth is wanted instead of deciding for them, and whether you stop when the answer is complete rather than continuing until someone interrupts
- Whether you can be redirected in the middle of an answer without restarting it from the beginning
- Whether a question outside your experience gets an honest boundary followed by reasoning from what you do know, instead of a confident answer with nothing behind it
How to prepare
- Rehearse one project at three lengths, roughly thirty seconds, three minutes, and a full walkthrough at the depth of a design review, and practise switching between them when someone interrupts mid-telling
- Have someone ask you five questions of deliberately mixed type in one sitting without telling you the types, and score only whether you identified each one correctly before you started answering
- Draft the sentence you will use to check depth, along the lines of asking whether the short version is useful here or they want the detail, and use it in a real conversation this week so the day of the round is not its first outing
Whiteboard Coding
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.
In-Depth Design Discussions
reportedAn unlabelled round is first an information problem, and the cheapest information is free. Whoever schedules it can usually tell you how long it runs, who will be in the room and what they work on, whether you will be writing code and in what environment, and whether anything is being sent beforehand. Ask in writing so the answer is on record, then prepare for the two or three formats those answers still leave open instead of betting on one. What separates a strong candidate is not guessing right; it is having an opening that works whichever one it turns out to be.
What to demonstrate
- Whether you can start work from an ambiguous brief, since tolerating a vague scope without stalling is the same thing the job asks for
- Whether the questions you asked beforehand were ones that change your preparation, such as duration, medium and who is joining, rather than ones whose answers you could not have acted on
- Whether you adapt when the round turns out to be something other than what you were told, instead of spending the first ten minutes visibly recalibrating
How to prepare
- Send one short scheduling message asking four things: how long, who is joining and what they work on, whether you will be writing code and where, and whether to prepare anything in advance. Treat a vague reply as real information, since it means the round is loosely structured and you will be shaping it yourself.
- Write one opening that works in any of the formats still open: restate in your own words what you have been asked to do, then ask which of two directions is more useful to them. Say it aloud until it stops sounding recited.
- Set up for the two most likely formats before the call starts, with a blank editor in the language you would choose and a shared document you can type into, so a format surprise costs you nothing in the first minutes
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.
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.
Listing technologies instead of trade-offs
Name the property the design needs first, such as ordered range scans, multi-entity transactions, cheap appends, or a predictable p99, then pick something that provides it and say what it gives up in exchange. Almost any component is defensible once you state the requirement it satisfies and the one it sacrifices.
Retrying a write that is not safe to repeat
A timeout tells you nothing about whether the server applied the write, so a blind retry of a create or a charge can duplicate it. Either make the operation idempotent, with a caller-supplied key the server deduplicates on or a conditional update, or do not retry it; and use exponential backoff with jitter so the retries of many clients do not synchronise into a second outage.
Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.
Array-based searching and sorting (focus on moving from O(n) to O(log …
Array-based searching and sorting (focus on moving from O(n) to O(log n)).
Approach
- 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.
- Choose the data structure from the access pattern, not from familiarity.
Follow-up
- Which test case would catch an off-by-one here?
- How does this change if the input no longer fits in memory?
Time-series data structure implementation.
Time-series data structure implementation.
Approach
- Choose the data structure from the access pattern, not from familiarity.
- 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.
Follow-up
- How does this change if the input no longer fits in memory?
- Which test case would catch an off-by-one here?
Linked list and string manipulation problems.
Linked list and string manipulation problems.
Approach
- State the target complexity and say which constraint rules the naive version out.
- Walk one small example through your approach before writing the whole thing.
- Choose the data structure from the access pattern, not from familiarity.
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?
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.
Worked solution 40 min
- Intern three nodes: A is (AUTH1, MRN-77), B is (AUTH2, MRN-77), C is (AUTH1, MRN-90).
- Apply union(A, C) from a manual_linked row, pushing its undo entry.
- Apply union(A, B) from an auto_linked row with a high match score, pushing its undo entry.
- Query all three, then pop one undo entry and query all three again.
- Assert the parent and size arrays element-wise against a snapshot taken before the second union.
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?
Namespace source identifiers so a cross-facility join cannot collide
person_identity_link holds one row per (assigning_authority, source_person_id) per link version: link_id, enterprise_person_id, assigning_authority, source_person_id, match_score, link_status in ('auto_linked','manual_linked','potential_duplicate','unlinked','rejected'), version, superseded_by_link_id, decided_by, decided_at. Two facilities in one network both issued medical record number 004821, to different people. An existing extract joins on source_person_id alone and has been merging their charts. Give the constraint set that makes the bare join impossible to write by accident, then write the query that resolves one facility's (authority, MRN) to its current enterprise_person_id.
Approach
- Name the root cause precisely: an MRN is unique only inside the authority that issued it, and a member ID only inside payer plus plan. The identifier is half a key; the namespace is the other half.
- Make the composite the only addressable key. UNIQUE (assigning_authority, source_person_id, version) gives version history; a partial unique index on (assigning_authority, source_person_id) WHERE superseded_by_link_id IS NULL enforces one live decision per source record.
- Remove the bare column as a join target from every consuming view: expose a view or function that takes both arguments, so a one-argument lookup is a compile-time error rather than a silent cross-patient merge.
- Write the resolution query against the live version only, and filter link_status separately — 'unlinked' and 'rejected' are live rows that mean 'no enterprise identity', so they must not be confused with 'no row'.
- State the detection query you would run before trusting any existing extract: group source_person_id across authorities and count distinct authorities, which surfaces every collision already in the data.
Worked solution 20 min
- Write the two constraints and say which invariant each one holds: the three-column UNIQUE holds version history, the partial unique index holds single-live-decision.
- Write the resolution query filtering on superseded_by_link_id IS NULL plus the two linked statuses.
- Write the collision detector over the existing table and run it mentally against the 004821 case to confirm it returns that value with two authorities.
- Decide the contract for zero rows versus a live 'unlinked' row: zero rows means the source record was never seen, 'unlinked' means it was seen and deliberately not attached, and the caller must handle them differently.
- Write the one-line fix for the broken extract: add the authority column to the extract and to the join, then re-run and diff the row count against the old output.
Follow-up
- The spine stores enterprise_person_id directly on encounter and claim_line. What does a wrong merge cost you with that design, and what would you store instead to keep unmerge a supported operation?
- A link is downgraded from auto_linked to potential_duplicate. Should the resolution query return the old enterprise identity, nothing, or an error, and who decides?
- Your partial unique index blocks a second live row. How does the merge path insert the new version and supersede the old one without violating it mid-transaction?
Design a digital whiteboard with components (drawing, text, and annota…
Design a digital whiteboard with components (drawing, text, and annotation).
Approach
- Name the failure you are designing for, then the recovery path.
- State the consistency you need, and where you are willing to be stale.
- Choose a partition key and say what query it makes expensive.
Follow-up
- What breaks first when traffic grows ten times?
- What would you drop to keep the system up under load?
Core OOPS concepts and their application in real-world scenarios.
Core OOPS concepts and their application in real-world scenarios.
Approach
- Work from the requirement backwards to the design.
- State your assumptions explicitly before working the problem.
- 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?
Explain multithreading concepts.
Explain multithreading concepts.
Approach
- 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.
- Work from the requirement backwards to the design.
Follow-up
- How would you know your answer was wrong?
- What assumption would you test first?
Design an error taxonomy for a real-time eligibility check
The registration desk calls GET /eligibility?person_id=&plan_id=&service_date= before a visit. The service answers from locally held coverage_span rows or by an external inquiry that takes seconds and sometimes fails outright. Staff must then proceed, proceed with an unverified flag, or stop, and the screen cannot make that call on its own. Define the response and error taxonomy: which outcomes are 2xx with a payload, which are 4xx, which are 5xx, what each carries, which are safe to retry, and what the desk is instructed to do for each. State the timeout budget.
Approach
- Separate the three things a boolean conflates: covered, not covered, and could not determine. Only the first two are answers. Flattening the third into 'not covered' produces a wrong financial answer wearing the clothes of a determinate one, and the desk cannot tell the difference.
- Put every determinate and indeterminate answer on 200 with a discriminated payload: {determination: covered | not_covered | indeterminate, reason_code, as_of, computed_at, source: local | payer | cache, coverage_order, cost_share, next_action}. The transport status describes the call; the payload describes the coverage. A not_covered answer is a successful call.
- Reserve 4xx for caller faults a retry cannot fix and make them distinguishable, because the remedy differs: 404 unknown person, 422 malformed or out-of-range service_date, 403 when the access decision refuses the purpose of use. Nothing retryable belongs in 4xx.
- Treat an external timeout as an outcome, not a transport failure. Return 200 with determination=indeterminate, reason_code=payer_timeout, and the locally derived coverage_span answer as of service_date alongside it. A 504 here throws away a usable degraded answer the desk could act on. Keep 5xx for the case where even the degraded answer could not be produced.
- Fix the budget end to end and state who enforces it: a roughly 2s desk interaction budget, a hard 1500ms deadline on the external inquiry, the remainder for the local path, and a client timeout set strictly above the server's so the server decides the fallback rather than the client abandoning a call that was about to degrade correctly. Note that every outcome here is retry-safe because the call is a read whose only side effect is the access audit row.
Worked solution 20 min
- Write the full payload for a covered answer, including as_of, computed_at and the coverage_order that drives coordination of benefits.
- Enumerate every failure the call can hit - unknown person, bad date, consent refusal, no local data, payer timeout, payer error, our own fault - and assign each a status code and a reason code.
- For each row write the one sentence the desk sees and the action offered, then check that no two causes collapse into the same action.
- Do the deadline arithmetic: total budget, external deadline, remaining local budget, and the client timeout that must exceed the server's.
- State the retry rule per class: retryable, not retryable, or retryable with backoff only.
Follow-up
- Two payers answer and disagree about which is primary. What does the endpoint return?
- How long may an eligibility answer be cached, and what exactly is the cache key?
- The desk proceeded on an indeterminate answer and the claim denies six weeks later. What in the response makes that decision reconstructable?
Evening eligibility denials cluster in western time zones
Eligibility denials rose from 0.4% to 3.1% of responses. They cluster after 17:00 local time, only at facilities in UTC-5 through UTC-10, and spike on the first and last day of each month. coverage_span.effective_date and termination_date are date columns in business time; the service derives the service date from encounter.admit_ts, a timestamptz. The enrolment files for the affected members look correct on inspection. Give the ordered diagnosis and the fix.
Approach
- Pivot the denial rate two ways before reading code: by facility UTC offset and by local hour of day. A defect that tracks local hour and offset is a time-conversion bug; one that tracks a source file, payer or plan is a data bug. The month-boundary spike is a consequence rather than a second problem, because terminations cluster on month ends and a one-day skew is most visible there.
- Take one denied request and recompute it by hand: the raw admit_ts, its UTC calendar date, the facility-local calendar date under the facility's IANA zone, and the coverage row's effective_date and termination_date. If the answer flips between the UTC date and the local date, the conversion is the cause and no further hypotheses are needed.
- Find the cast. In PostgreSQL, timestamptz::date is evaluated in the session's TimeZone setting, so the identical SQL returns different dates on different pooled connections and a pool that inherits UTC yields the UTC calendar date. date columns carry no zone at all, so comparing a date column to a UTC-derived date compares two different calendars.
- Fix once, at the edge: resolve the service date as (admit_ts AT TIME ZONE facility.iana_zone)::date and pass it down as an explicit date parameter. Use the IANA zone name, never a stored numeric offset, because the offset changes twice a year under daylight saving and a stored -8 is wrong for roughly eight months.
- Forbid re-derivation below the edge and pin the boundary with a frozen-clock test: an encounter at 23:30 local at a UTC-10 facility on the last day of a month whose coverage terminates that day, plus the same encounter one minute later. A test that calls now() passes for most of the day and hides the defect.
Follow-up
- Where else does the as-of date enter the system — the eligibility cache key, claim_line.service_from_date, the accumulator's plan year? Which of those are already computed in UTC?
- An inpatient encounter spans midnight local. Which date governs eligibility — the admit date or the service line date — and at which layer is that decided?
Roughly ninety minutes on weeknights with one longer weekend block. The plan cuts scope rather than compressing everything, on the assumption that one thing finished per night beats four half-started.
Prepare, practise & reflect
One practical outcome each day. Spend longer where you need it.
0 / 7 done01Fix the scope and take a cold baseline
- Read the role description and write the three things the loop will almost certainly test, then write an explicit not-doing list and keep it visible all week.
- Take one twenty-five-minute coding problem and one fifteen-minute design prompt cold, and write the single sentence naming what blocked each, because those two sentences decide where the remaining evenings go.
- Set the week's rule: one thing finished every night, including the night you only have forty minutes.
Deliverable: A one-page scope with a not-doing list and two cold attempts, each carrying one sentence on what blocked it.
Practice prompt ↗Practice prompt ↗Worked solution ↗02One pattern, written three times from blank
- Choose the single pattern most likely to appear in your loop and write it three times from an empty file rather than editing the previous attempt.
- On the third pass, write the invariant as a comment before the loop body and the complexity before the first line of code.
- Stop at ninety minutes even if the third version is imperfect, and write the one thing you would fix given another hour.
Deliverable: Three independent implementations of the same pattern plus a note on what changed between them.
Practice prompt ↗Practice prompt ↗03One design, only to the depth you can defend
- Take one system shape and go only as far as requirements, interface and data model, refusing to draw a box you could not survive a follow-up about.
- Attach one number to each non-functional requirement, deriving it rather than asserting it, and write the assumption the number rests on.
- Write the one tradeoff you are choosing against and the observation that would make you reverse it.
Deliverable: One design at interface-and-schema depth with derived numbers and one written reversible tradeoff.
Practice prompt ↗Practice prompt ↗04Only the fundamentals you will have to defend
- Write, in under two hundred words each, the answers to the two questions that follow almost any implementation: why this structure and not the obvious alternative, and what happens to this code at a hundred times the input.
- Write what an index actually costs: faster lookups on the indexed columns against a write that now maintains a second structure, plus the cases where the planner declines to use it anyway, low selectivity, or a predicate wrapping the column in a function.
- Delete any answer you cannot deliver aloud in under a minute, since an answer that needs reading is not an answer you have.
Deliverable: Three written answers, each under two hundred words and each timed aloud.
Practice prompt ↗Practice prompt ↗Worked solution ↗05Your own work, timed
- Write a ninety-second and a four-minute version of your main project and time both aloud rather than reading them.
- Prepare the two follow-ups that always come: what you would do differently, and how you knew it worked.
- Put one number in the first sentence and be ready to say exactly where it came from and what it excludes.
Deliverable: Two timed narratives with one defensible number in the opening line.
Practice prompt ↗Practice prompt ↗06The one full rehearsal, in the weekend block
- Run a sixty-minute mock covering a coding round and a design round in one sitting with no break, because sustained attention is the thing evenings have not trained.
- Immediately afterwards, and before hearing any feedback, write the three moments you lost the thread.
- Spend the rest of the block only on those three moments, and on nothing you merely feel shaky about.
Deliverable: Mock notes naming three failure moments with a specific fix written under each.
Practice prompt ↗Practice prompt ↗07Taper
- Write the twenty-minute warm-up you will actually do on the morning: one problem you can already solve from a blank file, one design you can narrate, and nothing you have never seen.
- Re-read only your own notes from this week and open no new material.
- Write the logistics down: the editor or shared document you will be working in, whether execution and lookups are permitted, and the sentence you will use when you do not know something.
Deliverable: A one-page card holding the design structure, the project numbers, and the logistics.
Practice prompt ↗Practice prompt ↗Worked solution ↗Expand any day for tasks and deliverables. Your progress is saved on this device.
A migration is a cost you chose to pay, not an achievement. The story is what the old system made expensive, what you measured before committing, what kept serving traffic during the cutover, and what you would have done if the numbers had come back flat. Without those, a rewrite reads as taste.
Describe a time you faced a challenging work situation and how you res…
Describe a time you faced a challenging work situation and how you resolved it.
Approach
- Pick a story where you made the decision, not one where you watched it.
- Give the blast radius: what could have broken, and what you measured.
- Close with what you would do differently, concretely.
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 working in a team with conflicting personalities?
How do you handle working in a team with conflicting personalities?
Approach
- Give the blast radius: what could have broken, and what you measured.
- Close with what you would do differently, concretely.
- Name the disagreement and how you resolved it with evidence.
Follow-up
- What would you do differently if you ran that again?
- How did you know your change caused the improvement?
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
Describe a time you faced a challenging work situation and how you resolved it.
- 02
How do you handle working in a team with conflicting personalities?
- 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 Loop Health interview guide?
No. It is PracHub's own research and practice material for the Software Engineer role at Loop Health. Rounds and questions reflect what candidates have reported, not a process Loop Health 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?
The difficulty is generally perceived as medium to hard. The challenge often stems from the depth of optimization required rather than the complexity of the problems themselves.
PracHub interview research ↗What is the best way to prepare for the LLD rounds?
Focus on designing common system components, such as rate limiters or UI-based widgets, while paying attention to class diagrams and interface segregation.
PracHub interview research ↗Does the company value culture fit?
Yes, behavioral rounds are critical. Be ready to share specific stories about how you handled technical disagreements or personality conflicts in a professional, constructive manner.
PracHub interview research ↗What is the typical timeline for the process?
The process can span several weeks, including multiple technical and leadership rounds. Keep in mind that scheduling can vary based on internal hiring needs.
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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