As a Software Engineer at Quantum-Systems, you are at the intersection of cutting-edge aerospace technology and high-stakes software development. Quantum-Systems works in advanced aerial robotics, and this role is critical to making sure the company's autonomous systems operate with precision, reliability, and intelligence in complex, real-world environments.
Your work directly affects the core functionality of Quantum-Systems' platforms, whether you are developing perception algorithms, building automated testing frameworks for production, or managing the implementation of software that connects hardware to mission objectives. You will be part of a mission-driven team where the software you write translates directly into the successful flight and operation of the company's drones.
This role offers a unique opportunity to tackle challenges that are rare in the industry, such as working with limited compute resources, managing real-time data streams, and ensuring absolute safety and performance in the field. You will be expected to work in a fast-paced environment where your technical contributions shape the strategic direction of Quantum-Systems' products.
Screening Phase
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
Technical Evaluations
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
Behavioral Interviews
reportedWhat you say here is written down by each interviewer and compared afterwards, so the unit of evaluation is a claim someone else could check, not a well-told narrative. Two things make a story checkable: detail only a participant would hold, and a clean line around which part was yours. Vague ownership is the usual failure and it is usually accidental, because engineers say we about the team's work and we about their own, so the thing they personally built disappears into the plural. Name the part you wrote, and name who did the rest.
What to demonstrate
- Whether your details are ones a participant would hold and an observer would not: the constraint that ruled out the obvious approach, the first attempt that failed, the person who objected and on what grounds
- Whether ownership survives a direct question, since a follow-up to we decided is routinely who decided, and an answer that stays plural at that point is read as the work belonging to someone else
- Whether the numbers you quote are ones you would say identically to a former colleague with the dashboard open
How to prepare
- Go through each story replacing every we with either I or a named role (the on-call engineer, the reviewer, the other team) and check the story still holds together. Wherever it stops making sense you have found a part you cannot actually speak to
- Open the artefacts for two of your stories, the pull request, the design doc, the incident notes, and read them for dates and figures you have been rounding in the retelling. Correct your version to match
- For each story write the single sentence you would least want repeated to a former teammate, then either make it accurate or take it out
PracHub editorial advice for the preparation topics above.
Representing prices and quantities as binary floating point.
IEEE-754 binary64 cannot represent 0.01 exactly, so a cost basis accumulated in doubles drifts, and an equality test against a tick boundary fails unpredictably. The fix is an integer at a fixed scale - a count of ticks, or a scaled integer at the instrument's price_scale - which also turns the tick-size check into a modulus rather than a tolerance comparison. The nuance that lets this trap survive code review: binary64 represents every integer exactly up to 2^53, about 9.0e15, so a scaled integer carried in a double is exact right up until it is not, and the first failure tends to be a large notional in a low-priced instrument, in production.
Reading the wall clock inside strategy or matching logic instead of taking time from the event stream.
It breaks replay outright - the same inputs stop producing the same outputs - and it adds a subtler failure. CLOCK_REALTIME is adjustable, so an NTP correction can step it backward and produce a negative interval or a timer that fires twice. CLOCK_MONOTONIC never steps backward but is not comparable across machines and, on Linux, does not advance while the machine is suspended, so it is the right clock for measuring a duration and the wrong one for stamping an event. Event time has to flow through the system as data, with the wall clock consulted only at the ingestion boundary where a receipt timestamp is taken once and then carried.
Assuming the bug is in the framework
Suspect your own code first: read the stack trace top to bottom, check which versions are actually installed rather than which ones you believe are, and reproduce in isolation before blaming a library that thousands of people run daily. When the fault really is upstream, you need that minimal reproduction to say so credibly anyway.
Trusting input because it came from your own front end
Anything crossing a trust boundary is hostile: parameterise queries instead of building SQL by concatenation, validate against an allow-list rather than a deny-list, and bound the size of anything you allocate from a request. Raising this unprompted in an API or design question is a cheap and unusually strong signal.
Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.
Resolve position identity across a corporate action graph
Corporate actions are edges (from_instrument_id, to_instrument_id, action_type in symbol_change, merger, spinoff, expiry_roll, ratio_num, ratio_den, effective_ts) — up to 2 x 10^6 edges over 10^6 instruments. Given a position in instrument I held at t0, return the set of (instrument_id, exact rational multiplier) that it becomes at t1 > t0, following only edges whose effective_ts lies in (t0, t1]. Mergers give a node in-degree above one; spinoffs give out-degree above one. Detect cycles and report them as data errors rather than breaking them. Give both the per-query and the batch complexity.
Approach
- Start from what the shape of the answer has to be. A spinoff makes out-degree greater than one, so a position maps to a set, not to a single successor; a merger makes in-degree greater than one, so the reverse map is not a function. Anything that collapses each instrument to one representative — union-find being the usual reflex — cannot express either, and also destroys the as-of property that makes the query meaningful.
- Filter the edge set to effective_ts in (t0, t1] and traverse forward from I with DFS, carrying an exact rational multiplier along each path as a reduced (num, den) pair. Multiply at each hop, run gcd immediately to keep the components small, and use checked 128-bit multiplication so a long chain fails loudly rather than wrapping.
- Accumulate at the terminals by summing multipliers across distinct paths that reach the same instrument, because a spinoff that later re-merges into its parent genuinely contributes twice. Summing rationals means a common denominator, then a gcd reduction again.
- Run the cycle check before the path-carrying traversal, not after it. The DFS above carries a multiplier down every distinct path and so does not terminate on a cyclic subgraph at all; the traversal's termination is a precondition that Kahn establishes, not a property the traversal has on its own.
- Complexity: a single query is O(V + E) in the worst case. For a batch — end-of-day, every position in the book — sort nodes by effective_ts to get a topological order of the filtered subgraph and memoise the resolution per (node, t1), which makes the whole batch O(V + E) once instead of O(positions x (V + E)).
- Detect cycles with Kahn's algorithm on the filtered subgraph: after the queue drains, the residual set is exactly the nodes on or downstream of a cycle. Run Tarjan on the residual to name the strongly connected components of size above one, report them with the offending edges and exit non-zero. Time is monotone along real corporate actions, so a cycle is always a vendor or load error; deleting an edge to make the traversal terminate hides the error and leaves positions mapped to the wrong instrument.
- Keep ratios rational end to end. A 1/3 hop followed by a 3/1 hop — a consolidation and then a re-split, through distinct instruments — must compose to exactly 1/1, which it does with reduced rationals and does not with binary64, where the round trip lands near 0.9999999999999998 and a position quantity is then off by a share after rounding.
Follow-up
- A vendor correction arrives at 18:00 changing yesterday's merger ratio. What do you recompute, and what does that do to already-published positions?
- A cash-in-lieu component means the mapping is not purely quantity-to-quantity. Where does that land in the model?
- How do you make this resolution replayable, so a backtest run in six months resolves the same identity the live session did?
Publish top instruments by traded notional every second
Fills stream in as (instrument_id, last_qty, last_px, contract_multiplier), with last_px an integer at the instrument's price_scale. Roughly 10^7 fills a day across 10^5 instruments. Publish the fifty instruments with the highest cumulative traded notional for the session, refreshed once a second, using exact integer arithmetic. Per-fill work must be O(1), and the per-second emit must not rescan all 10^5 instruments in the common case. Trade cancels and corrections can reduce an instrument's total. Give the accumulator width you need, and the argument that your pruning cannot drop a qualifying instrument.
Approach
- Size the accumulator before choosing the algorithm. A single fill of 10^6 lots at a price of 100 carried at price_scale 9 is 10^6 x 10^11 = 10^17 in scaled units, so an int64 session total overflows after about 92 such fills (9.22e18 / 1e17). Accumulate in int128, or rescale to a coarser notional unit at ingest and document the rounding; a double is out on the usual grounds, since it is exact only to 2^53.
- Per fill: one open-addressed hash map lookup on instrument_id, one 128-bit add, and an insert into a dirty set — O(1) expected, no allocation, no ordering work at all between emits.
- Per emit: maintain tau, the current fiftieth-largest total. Candidates are the previous top fifty plus the dirty instruments whose total exceeds tau. Select with a bounded min-heap of size 50, which is O(c log 50) for c candidates, and c is a few thousand per second rather than 10^5.
- State the precondition that makes the pruning sound: while totals only increase, an untouched instrument below tau cannot have crossed it, because nothing changed its value. Cancels and corrections break exactly that precondition, so a corrected instrument must enter the dirty set and, if it falls out of the top fifty, the replacement may be an instrument the pruning skipped.
- Close that hole rather than ignoring it: keep a reserve of the top 200 and refill from it when a member drops, falling back to a full O(m) quickselect over all 10^5 totals when the reserve is exhausted. The fallback runs rarely, is bounded, and is the reason the output is correct rather than usually correct.
- Report both costs: amortised O(1) per fill, O(c log k) per emit with an O(m) worst case, and note that the emit runs on its own thread off a snapshot so it never sits in the fill path.
Follow-up
- Make it a rolling five-minute window rather than a session total. What state does each instrument now need, and what does that do to memory at 10^5 instruments?
- Two instruments tie exactly at the fiftieth place. What is your tie-break, and why does an unstable one make the output non-reproducible in replay?
- The totals are in mixed currencies. Where does the FX conversion go, and what happens to the ranking when a rate updates mid-second?
Find every overlapping effective window before adding the constraint
instrument_version holds about 10^7 rows of (instrument_id, effective_from, effective_to nullable). Versions for one instrument_id must never overlap, and at most one may have effective_to NULL. Adding the EXCLUDE constraint aborts on the first conflicting pair it meets, so you need the whole picture first. Produce every row that overlaps another row for its instrument, per-instrument overlap counts, and every instrument holding more than one open-ended row. The median instrument has about ten versions; the worst has 10^5 after repeated vendor reloads. Explain why the pairwise comparison is correct but cannot ship.
Approach
- Cost the naive version properly rather than dismissing it. Grouped by instrument_id it is the sum of g^2 over groups, and at a median g of ten that is trivial — the blow-up lives entirely in the tail, where one 10^5-version group contributes 5 x 10^9 comparisons. Worse, its output is also quadratic: if that group is a repeated reload, nearly every pair overlaps, so the self-join emits billions of rows describing a few thousand bad versions. The output size, not the runtime, is the reason it cannot ship.
- Restate the deliverable as rows rather than pairs, which is what makes a linear output possible, then sort by (instrument_id, effective_from, effective_to nulls last) for O(n log n) and sweep each group once.
- In the sweep, carry running_max_end = MAX(COALESCE(effective_to, infinity)) over the rows already seen in this group, plus the row that set it. A row overlaps iff its effective_from is strictly less than running_max_end; flag both that row and the max-setter, so both sides of every overlap appear in the output. O(1) extra state per group.
- Do not shortcut to comparing adjacent rows only. It is sufficient to answer the boolean does any overlap exist — if every adjacent pair is disjoint then end_i <= start_(i+1) <= start_j for all j > i, so nothing overlaps — but it misses nested intervals when you must list every offending row, and a long open-ended version nesting later ones is the single most common real shape.
- If you write it in SQL, the window-function form is MAX(COALESCE(effective_to, 'infinity'::timestamptz)) OVER (PARTITION BY instrument_id ORDER BY effective_from ROWS BETWEEN UNBOUNDED PRECEDING AND 1 PRECEDING). The COALESCE is load-bearing, and in two distinct ways: MAX ignores NULLs, so an earlier still-open version contributes nothing to the running maximum, and where it is the only preceding row the maximum is NULL outright, which makes effective_from < running_max evaluate to NULL rather than true. Either way the comparison is not true and the overlap disappears from the report.
- Handle the open-row rule as its own aggregate in the same pass — COUNT(*) FILTER (WHERE effective_to IS NULL) > 1 per instrument — since a second open row is a violation whether or not your range model makes it overlap, and since it is the only part of the report that survives the COALESCE being dropped. Only then add EXCLUDE USING gist (instrument_id WITH =, tstzrange(effective_from, effective_to) WITH &&), which needs btree_gist for equality on a bigint, and expect to iterate because each failed build names one pair.
Worked solution 35 min
- Build instrument 7 with r1 = [2026-01-01, NULL), r2 = [2026-03-01, 2026-04-01), r3 = [2026-05-01, 2026-06-01), and instrument 9 with two rows both open from 2026-01-01 and 2026-02-01.
- Run the sweep with COALESCE to infinity and record which rows are flagged for each instrument.
- Run the same sweep with plain MAX(effective_to) and diff the two outputs, evaluating the comparison for r2 and r3 separately.
- Run an adjacent-pairs-only variant and diff again.
- Attempt to add the EXCLUDE constraint on the unfixed fixture and record what it reports.
Follow-up
- tstzrange(a, a) is empty and empty ranges never overlap, so a zero-width version passes the constraint. Is that acceptable, and what would catch it?
- The fix requires closing thousands of rows. How do you do that without editing history that a replay depends on?
- How would you run this check continuously, so the next bad vendor load is caught at write time instead of at constraint-build time?
Changing a price column's type on a live four-billion-row table
execution_report.last_px is NUMERIC(18,6) and must become a BIGINT integer at the instrument's price_scale, which lives on the effective-dated instrument_version row. The table holds 4 x 10^9 rows, takes writes continuously through the session, and is replicated. Produce the migration plan with no write downtime: the exact DDL steps and the lock each takes, how the backfill is batched and throttled, how you verify before cutting reads over, and the rollback point at every stage. Name the correctness bug specific to resolving price_scale during the backfill.
Approach
- Add the column cheaply and defensively. ALTER TABLE ... ADD COLUMN last_px_scaled BIGINT is a catalog-only change in PostgreSQL 11 and later for a nullable column or a non-volatile default, so there is no table rewrite. It still takes ACCESS EXCLUSIVE briefly, and that lock queues ahead of every new query, so set lock_timeout to a couple of seconds and retry rather than letting one long-running transaction stall the entire table behind the ALTER.
- Start dual-writing before backfilling, from the application or a BEFORE INSERT trigger, so the backfill only ever has to catch up on a closed set of old rows instead of chasing a moving tail.
- Backfill in bounded batches over a key range, one commit per batch, throttled on replication lag and autovacuum progress. Every UPDATE writes a new row version, so a full-table backfill roughly doubles the physical size until vacuum reclaims it; unthrottled, it bloats the table and lags the replicas at the same time, and the second symptom hides the first.
- Resolve price_scale as of the execution's venue_ts, not as of today. instrument_version is effective-dated, so a scale change since the trade makes a today-resolved backfill mis-scale every historical row of that instrument by a power of ten, silently, because the values stay plausible. Assert per row that the stored NUMERIC carries no more decimals than the target scale, and fail the batch rather than round away real precision.
- Verify before cutting reads over: a full pass returning zero rows for last_px_scaled IS DISTINCT FROM round(last_px * power(10::numeric, price_scale)) (numeric power, not the float8 ^ operator), plus a zero count of NULLs, run twice with the second run after further writes have landed. Then move reads behind a flag, soak, and only then DROP COLUMN last_px, which is catalog-only with space reclaimed on the next rewrite.
- State the rollback at each stage: before dual-write, drop the column; during backfill, stop and the old column is still authoritative; after read cutover, flip the flag back because both columns remain populated; after the drop there is no rollback, which is why the drop is a separate change weeks later.
Worked solution 45 min
- On a copy of the table, time ADD COLUMN with and without lock_timeout while a long-running SELECT holds a conflicting lock, and watch the queue form behind the ALTER.
- Write the batch backfill keyed on a range, joining instrument_version as of venue_ts, and measure rows per second and table size growth over ten batches.
- Run the verification query over the backfilled range, then corrupt one row deliberately and confirm it is caught.
- Add the NOT VALID check constraint and VALIDATE it, timing both and confirming writes continue throughout.
- Write the rollback sentence for each stage and identify the one stage that has none.
Follow-up
- Where do you add NOT NULL, and what does SET NOT NULL cost on this table compared with a NOT VALID check constraint you validate afterwards?
- The backfill needs an index that does not exist yet. How do you add it without blocking writes, and what do you do when it fails halfway through?
- A replica serves reporting and cannot tolerate an hour of lag. What changes about the batch size and the schedule?
Derive end-of-day positions from executions including trade corrections
You have position_snapshot rows with snapshot_kind = 'sod_clearing' (account_id, instrument_id, business_date, net_qty) and execution_report (PRIMARY KEY (venue_mic, venue_exec_id), order_id, instrument_id, account_id, exec_type, last_qty, last_px, corrects_exec_id, source, venue_ts, received_ts). Write the query producing eod_derived net_qty per account and instrument for one business date. exec_type includes trade_correct and trade_cancel, which arrive as new rows referencing an earlier execution; the original row is retained. Side lives on order_event, not here. Then say why avg_cost_px cannot be computed in the same statement.
Approach
- Get side without fanning out: join each execution to the single order_event row with event_type = 'submit' for its order_id. Joining on order_id alone multiplies every fill by the number of lifecycle rows that order accumulated, which is five or more on a partially filled order.
- Reduce corrections to an anti-join instead of arithmetic. Exclude any execution whose (venue_mic, venue_exec_id) appears as the (venue_mic, corrects_exec_id) of a later row: a superseded fill then contributes nothing, a trade_cancel contributes nothing itself, and a trade_correct contributes its restated last_qty once. Match on venue_mic too, since venue_exec_id is only unique within a venue.
- Sum CASE WHEN side IN ('buy','buy_to_cover') THEN last_qty ELSE -last_qty END over exec_type IN ('partial_fill','fill','trade_correct'), grouped by (account_id, instrument_id), then FULL OUTER JOIN onto the sod_clearing rows with COALESCE on both sides so an instrument traded from flat and one closed to flat both appear.
- Scope the date by the venue's business date, not by received_ts::date in the server's timezone. A correction for yesterday arrives today and belongs to yesterday.
- Do not dedupe in the reader. The primary key already makes a resend from drop_copy or the clearing file a no-op at write time (ON CONFLICT DO NOTHING), so a DISTINCT in the query is either redundant or hiding a schema problem.
- Answer the avg_cost_px question precisely: average cost depends on the running position at the moment of each fill, because a reducing trade leaves it unchanged and realizes P&L while a sign flip resets it. That is a sequential fold with carry, not an aggregate. SUM(...) OVER (ORDER BY ...) gives running quantity but cannot carry the average, so it needs a recursive CTE or an application-side fold.
Follow-up
- A resent report for an execution already inserted from the trading session arrives on the drop copy. What does your write path do, and what does the reader have to change?
- Make this incremental so the intraday_derived row refreshes every few seconds without rescanning the day. What does the watermark have to be ordered by?
- The derived total differs from eod_reconciled by 100 shares in one instrument. What do you do to the derived row?
Describe a situation where you had to pivot your technical approach du…
Describe a situation where you had to pivot your technical approach due to unexpected hardware limitations.
Approach
- Fix the scope first: who calls this, how often, and what they do when it fails.
- Name the failure you are designing for, then the recovery path.
- Choose a partition key and say what query it makes expensive.
Follow-up
- What would you drop to keep the system up under load?
- How does this behave when that dependency is down for an hour?
What strategies do you use to identify bottlenecks in an automated tes…
What strategies do you use to identify bottlenecks in an automated testing or deployment pipeline?
Approach
- Choose a partition key and say what query it makes expensive.
- Name the failure you are designing for, then the recovery path.
- Fix the scope first: who calls this, how often, and what they do when it fails.
Follow-up
- What would you drop to keep the system up under load?
- What breaks first when traffic grows ten times?
Walk me through the architecture of a recent project you led or contri…
Walk me through the architecture of a recent project you led or contributed to significantly.
Approach
- Name the read and write paths separately; they rarely have the same bottleneck.
- Fix the scope first: who calls this, how often, and what they do when it fails.
- Choose a partition key and say what query it makes expensive.
Follow-up
- What would you drop to keep the system up under load?
- How does this behave when that dependency is down for an hour?
How do you ensure the reliability and safety of software that controls…
How do you ensure the reliability and safety of software that controls physical hardware?
Approach
- Work from the requirement backwards to the design.
- Clarify what is being asked and what a complete answer contains.
- State your assumptions explicitly before working the problem.
Follow-up
- How would you know your answer was wrong?
- What assumption would you test first?
Can you explain your approach to debugging complex integration issues …
Can you explain your approach to debugging complex integration issues between hardware and software?
Approach
- Say what you would check first and why it is the highest-information step.
- Clarify what is being asked and what a complete answer contains.
- State your assumptions explicitly before working the problem.
Follow-up
- How would you know your answer was wrong?
- What assumption would you test first?
Signed drop-copy webhooks with at-least-once delivery
A clearing broker pushes execution reports to your HTTPS endpoint rather than letting you poll. Each POST carries venue_mic, venue_exec_id, exec_type, cum_qty, leaves_qty and corrects_exec_id, plus signature and timestamp headers. Delivery is at-least-once and unordered, and any non-2xx is retried with backoff. Specify signature verification, the accepted timestamp window, what you return and how quickly, and how the handler stays correct when the same execution arrives twice and when a trade_correct arrives before the fill it corrects.
Approach
- Verify over the raw request bytes before parsing: HMAC-SHA256 over timestamp, a separator, and the body exactly as received, compared in constant time. Parsing and re-serializing JSON changes key order and whitespace and will fail a signature that is actually valid, so the framework must hand you the buffered body.
- Support two active secrets at once so rotation is an overlap rather than an outage, and accept a signature matching either.
- Bound replay with a timestamp tolerance (a few minutes, sized to your clock-skew budget plus the sender's retry backoff): outside it, reject. This bounds how long a captured request stays useful; it does not replace idempotency, because redelivery inside the window is legitimate.
- Acknowledge fast and apply asynchronously: return 2xx once the raw message is durably enqueued, not once it is folded into a position. The sender's retry timer is shorter than your downstream pipeline, and a non-2xx means 'send it again', so never return 500 for a message you have already accepted.
- Make the sink idempotent on (venue_mic, venue_exec_id) with an upsert, and make it order-insensitive by taking the venue's cum_qty and leaves_qty as authoritative rather than incrementing a local counter. Unordered delivery then costs nothing.
- Handle corrections as arrivals, not edits: a trade_correct or trade_cancel names corrects_exec_id, which may reference a row you have not received. Store it pending and resolve on arrival of the original, and keep the original row so the fold sees a negating entry instead of an in-place update.
Worked solution 30 min
- Write the verification function signature showing it takes raw bytes plus headers, and the constant-time comparison call.
- Write the handler as ordered steps with the exact point at which 2xx is returned, and label what has been made durable by then.
- Write the upsert on (venue_mic, venue_exec_id) and trace two deliveries of the same report through it.
- Trace a trade_correct arriving before its original and write the pending-resolution rule.
- List the status codes you return and what each instructs the sender to do.
Follow-up
- The sender is down for two hours. How do you detect that, given that no request arriving looks exactly like no executions happening?
- Your endpoint returns 200 but the enqueue silently drops. What end-of-day control catches it?
- Would you let the sender's payload carry your position directly rather than the execution? What breaks?
Fills land on the wrong business date twice yearly
Reconciliation reports non-zero break_qty on a handful of (account_id, instrument_id) rows in position_snapshot, always for venues in one region, and the count spikes on the two weekends when that region's clocks change. business_date is derived in the fold by casting received_ts to a date. Executions carry venue_ts and received_ts as TIMESTAMPTZ(6). Give an ordered checklist that identifies the rule actually being applied, proves it against the break rows, defines the correct rule, and remediates deterministically.
Approach
- Gather evidence before forming a theory. Pull the breaking executions and, for each, compute business_date under every candidate rule - a cast of received_ts, a cast of venue_ts, the venue's session day from the trading calendar - and find which rule reproduces the date the row actually received. Exactly one will, and that ends the speculation.
- Inspect the cast's hidden input. In PostgreSQL a timestamptz cast to date is evaluated in the session's TimeZone setting, so the same row yields different dates for two clients and for a job whose connection settings changed. Confirm what the fold's session is actually set to rather than what the deployment document claims.
- Check column types along the whole path. A TIMESTAMP WITHOUT TIME ZONE anywhere upstream has already discarded the offset, and everything downstream of it is guessing. That is a distinct defect with the same symptom and wants finding now rather than after the first fix.
- Rule out the clock-step failure, which presents similarly. If any timestamp is taken from an adjustable wall clock inside the pipeline rather than carried from the ingestion boundary, a backward time correction produces negative intervals and duplicate ordering. Test for it directly by looking for out-of-order received_ts within one session.
- Define the correct rule. A business date is a property of the venue's trading session, resolved from the trading calendar in the reference service keyed on (venue_mic, venue_ts), not a property of any single time zone. Sessions cross local midnight for some contracts and cross UTC midnight for most venues outside one region, so no fixed offset and no single cast is correct - and a fixed offset additionally fails for the weeks each year when two regions' transition dates differ.
- Remediate deterministically. Recompute the affected snapshots from execution_report using fills_applied_through_exec_id as the watermark, keep the clearing and derived rows as separate snapshot_kind values, and let reconciliation confirm the breaks close instead of setting derived equal to clearing.
Follow-up
- A session opens at 17:00 local and closes at 16:00 the following day. What is business_date for a fill at 23:50, and where does that definition live?
- Two services disagree about a business date for the same execution. Which is wrong, and how do you make the disagreement impossible rather than rare?
- Why does storing everything in UTC not by itself solve this?
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 ↗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.
When the requirements were thin, the interesting part is how you fenced the problem off: the assumption you wrote down, who you got to confirm it, the narrow version you shipped first so the rest stayed cheap to change. Guessing and being right is luck. Guessing in writing, where someone could correct you, is method.
Describe your experience working in an agile or fast-evolving developm…
Describe your experience working in an agile or fast-evolving development environment.
Approach
- Close with what you would do differently, concretely.
- 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?
What is your experience with sensor fusion or computer vision pipeline…
What is your experience with sensor fusion or computer vision pipelines in an autonomous context?
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.
- Give the blast radius: what could have broken, and what you measured.
Follow-up
- How did you know your change caused the improvement?
- What did you decide not to do, and why?
Give an example of how you have contributed to improving team processe…
Give an example of how you have contributed to improving team processes or engineering culture.
Approach
- State the situation in two sentences and spend the rest on the reasoning.
- Give the blast radius: what could have broken, and what you measured.
- Name the disagreement and how you resolved it with evidence.
Follow-up
- What would you do differently if you ran that again?
- How did you know your change caused the improvement?
- 01
Describe your experience working in an agile or fast-evolving development environment.
- 02
What is your experience with sensor fusion or computer vision pipelines in an autonomous context?
- 03
Give an example of how you have contributed to improving team processes or engineering culture.
Is this an official Quantum-Systems interview guide?
No. It is PracHub's own research and practice material for the Software Engineer role at Quantum-Systems. Rounds and questions reflect what candidates have reported, not a process Quantum-Systems has published, and they change over time. Confirm the current format and scope with your recruiter.
PracHub interview research ↗How long does the entire interview process usually take?
The timeline varies, but from the initial screen to a final decision, candidates should expect a process that spans several weeks to allow for thorough evaluation.
PracHub interview research ↗Is there a specific coding language I should focus on?
Quantum-Systems uses a variety of tools, but C++ and Python are central to its robotics and perception work. Focus on demonstrating deep proficiency in the language you are most comfortable with.
PracHub interview research ↗What is the company culture like at Quantum-Systems?
The team is described as mission-driven and fast-paced. Candidates report that the interviews weigh accountability, direct communication, and a shared commitment to aerial robotics.
PracHub interview research ↗How much of the work is remote versus onsite?
The nature of working with physical hardware often requires onsite collaboration at Quantum-Systems' Gilching or Rostock locations.
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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