10x Genomics Software Engineer Interview Guide

The 60-second brief

Candidates describe 10x Genomics as a company that builds instruments and analysis software for studying biology at single-cell and spatial resolution. Reports place the Software Engineer role on two kinds of code. One is the software that runs on and around the instruments, down to low-level drivers. The other is the pipelines that process the genomic data those instruments produce. The work is reported as cross-disciplinary: engineers work with biologists, optical engineers, hardware engineers and data scientists, and turn scientific requirements into software specifications. That is why so many of the reported questions deal with long-running processes, hardware faults and high data volumes, and few deal with isolated puzzles.

This guide is for engineers interviewing for a Software Engineer opening at 10x Genomics, on either the instrument side or the data-pipeline side. It covers the three rounds candidates report and the nine reported questions, each with an answer outline and likely follow-ups. It also covers the requirements candidates describe: C++ or Python, multithreading and concurrency, and distributed-systems or embedded experience, with biotech or medical-device work, cloud infrastructure and large-scale data processing frameworks listed as pluses. Finally, it covers topics that show up across reported loops: algorithmic and live coding, React front-end work, behavioral questions and a technical project presentation.

Candidates report three rounds over roughly 3-5 weeks: a Recruiter Screen, a Technical Deep Dive with an engineer or hiring manager, and a Virtual Onsite Panel.

Focus areas

• Behavioral Interviewing
• Coding Interviews (Algorithmic Problem Solving)
• React (Front-End Development)

Overview

Most of the reported questions are about systems work, not standalone puzzles. They cover applying multithreading to high-throughput processing, how RAII and smart pointers affect software that runs for a long time, a data pipeline that survives connectivity drops and hardware errors, a circular buffer for real-time ingestion, cutting time complexity under constraints, and debugging a race condition. The technical areas candidates list point the same way: mutexes and atomics, memory and performance tuning, design patterns in instrument software, microservices versus monoliths in a lab setting, API design, inter-process communication, and data persistence. Reciting definitions will not be enough. You need to be able to write concurrent C++ or Python and explain why it is correct.

The reported scenarios also lean on your own history. Examples include explaining what context switching cost in systems you built, keeping data consistent under heavy concurrency, designing a system that logs and processes terabytes of sequencing data, and describing a refactor you did for performance. Pick one or two past projects you can take apart in detail: why that language, why that data structure, which threading model you used and why, what you measured, and what you would change.

The reported topics are broader than the requirements suggest. They include algorithmic coding, live coding, React front-end development and project presentations, and some candidates report a gap between the job posting and what the interviews covered. Use the recruiter screen to find out which of these apply to your team. A front-end opening moves React up your list. An instrument-side opening makes C++ concurrency and memory ownership the core of your preparation.

Because the data comes from physical instruments, failure handling comes up throughout the questions: dropped links, faulty hardware, processes that run for days, and buffers that fill faster than they drain. In any design or coding answer, say what happens when the input stops arriving, arrives twice, or arrives corrupted.

The 10x Genomics interview process

Three rounds are reported, from a recruiter call through a technical deep dive to a virtual panel. Candidates also mention preparing a project presentation for the later stages, so have it ready before the panel.

Recruiter Screen

Candidates describe this as an initial call with a recruiter about fit and about the role itself. It is also your best chance to learn what the rest of the loop looks like. Some candidates report that the interviews did not match the job posting, so ask which team the opening is on, whether that team works mainly in C++, Python or React, whether coding is done live, and whether a project presentation comes later. Confirm location as well. Reports say many engineering positions are based in Pleasanton, CA, because the work sits close to hardware and lab teams, and some roles offer more flexibility.

What to demonstrate

• Fit between your experience and the opening as the recruiter describes it
• Whether you can summarise your past work and your reasons for wanting this role clearly
• Logistics such as location and timeline

How to prepare

• A short walkthrough of your background that opens with your concurrent, embedded, distributed or data-pipeline work, the must-have areas candidates report.
• A written list of questions for the recruiter: team, primary language (C++ or Python), whether React is in scope, how live coding works, and whether a presentation is expected.
• Your location constraints, checked against an on-site expectation in Pleasanton, CA.

Technical Deep Dive

Candidates report an in-depth technical interview with an engineer or a hiring manager. Reports do not say which topics fall in which round, so bring the full technical list to this one: concurrency and synchronization primitives, memory management and performance, design patterns in instrument software, API design and inter-process communication, and data persistence. Tie every concept to something you built. If atomics come up, describe where you used one, what it replaced and how you knew it was correct. Before writing any code, state your assumptions and edge cases out loud, then name the complexity of what you are about to write.

What to demonstrate

• Technical skill in depth, assessed by an engineer or a hiring manager
• How clearly you explain the reasoning behind technical choices, including those in your own past systems

How to prepare

• Write a bounded producer-consumer queue in your main language from memory, and be able to justify every lock, condition variable and wake-up check in it.
• Pick one past system and write down its threading model, where context switches and lock contention occurred, and what you measured.
• Rehearse the acquire/release memory-ordering argument for a single-producer single-consumer queue, and the ownership rules for unique_ptr, shared_ptr and weak_ptr.

Virtual Onsite Panel

Candidates describe the final stage as a virtual panel discussion that assesses overall fit and potential. Reports do not break the panel into named sessions. Across reported loops the topics include behavioral questions, algorithmic and live coding, React front-end work and a technical project presentation. Candidates also mention preparing a presentation for the later stages, and since this is the last stage, have it ready before the panel. Prepare for both a technical and a behavioral side: a coding setup you have tested for live work, and stories that cover each reported behavioral prompt.

What to demonstrate

• Overall fit and potential, judged by several interviewers, as candidates describe the stage
• How well you communicate your work to a group, including a project presentation if your loop has one

How to prepare

• A project presentation about one system you owned: the problem, your design decisions, the alternatives you rejected, and measured results, with your contribution clearly separated from the team's.
• STAR stories for the three reported behavioral prompts, each with a concrete outcome and something you would do differently.
• Live coding practice in a shared editor in your main language, stating constraints and target complexity before typing.

Where candidates lose points

Preparing for only one stack and being caught out by React or live coding in a loop expected to be C++ or Python

Some candidates report that the interviews did not match the posting, and React and live coding appear among the reported topics. Ask the recruiter which team you are interviewing for, its main language and the coding format. Split your preparation by the answer, and keep a basic React refresher ready even for a backend-leaning role.

Writing a circular buffer that cannot tell full from empty or breaks at the wrap point

Choose the full/empty strategy (count, unused slot, or monotonically increasing counters with a power-of-two mask) before writing code, and say it out loud. Test pushes and pops that cross the end of the array, a buffer of capacity one, and the overflow policy you chose.

Answering the race-condition question with 'add a lock' or 'add a sleep' and no plan to reproduce or verify

Go in order: reproduce under stress, confirm with ThreadSanitizer or Helgrind, name the broken invariant, apply the smallest fix that covers it, then rerun the stress loop and add a regression test. Explain why volatile in C++ and the GIL in Python do not make the code safe.

Designing the fault-tolerant pipeline with retries but no idempotency, so a retry duplicates or corrupts data

Give every chunk a stable key and a checksum, spool locally before acknowledging, and make the receiver ignore a repeated key with a matching checksum. Then answer the questions that remain: what happens when the spool fills, and how a hardware fault is flagged rather than passed along.

Giving a project presentation that tours the team's system without your own decisions in it

Build the presentation around choices you made: the alternatives, why you rejected them, and the measurement that confirmed the result. Mark clearly which parts you built yourself, and prepare for questions on any number you show.

Questions & practice

How does your experience with multithreading and concurrency apply to high-throughput data processing?

Medium · Technical & Domain Expertise

Approach

1. Open with a concrete structure: a staged pipeline (read, parse, process, write) linked by bounded queues, with a worker pool per CPU-bound stage. The bounded queue gives you backpressure, so a slow writer stalls readers and memory stays flat.
2. Explain how you avoid shared mutable state. Partition the input into independent chunks, give each worker its own buffers and output, and merge at the end, so locks only guard queue hand-offs. Batch items per hand-off so the cost of synchronization is spread over many records.
3. Size the pools from the workload: about one thread per core for CPU-bound stages, more for I/O-bound ones. Use Amdahl's law to explain why the serial stage caps the speed-up. In Python, say that the GIL means CPU-bound work needs multiprocessing or native code that releases the GIL.
4. Cover ordering and failure. If output must keep input order, tag chunks with sequence numbers and use a reorder buffer at the sink. Say how a worker error stops the pipeline cleanly: a poison pill or a cancellation flag, with queues drained.
5. Back the answer with numbers from your own work: throughput before and after, where contention showed up, and how you found it (profiler, lock wait times, CPU use per core).

Follow-up

• Workers finish out of order but the output file must be in input order. How do you keep memory bounded while you reorder?
• How did you choose the thread count, and what happened when you doubled it?
• Two threads update counters that sit next to each other in memory and throughput drops. What is happening?

Can you explain how memory management (e.g., smart pointers, RAII) impacts the performance of long-running ins

Medium · Technical & Domain Expertise

Can you explain how memory management (e.g., smart pointers, RAII) impacts the performance of long-running instrument software?

Approach

1. Define RAII precisely: a resource's lifetime is tied to an object's scope, so the destructor releases it on every path, exceptions included. For instrument software that runs for days, this is how file handles, device handles, DMA or frame buffers and mutex locks get released even when error paths are rarely exercised.
2. Compare the costs. unique_ptr costs nothing over a raw pointer and makes single ownership explicit. shared_ptr adds a control block and atomic reference-count updates on every copy, so passing it by value in a hot loop has a measurable cost; make_shared uses a single allocation. Pass by reference, or pass a raw pointer, when the callee does not take ownership.
3. Name what goes wrong in long-running processes: slow leaks, including shared_ptr cycles (break them with weak_ptr), heap fragmentation from mixed allocation sizes, and allocation latency in real-time paths. The fixes are preallocated pools or arenas for fixed-size frames and no allocation in the acquisition loop.
4. Explain how you would verify it: soak tests that watch resident memory over hours, plus AddressSanitizer/LeakSanitizer, Valgrind or heaptrack to find where the memory goes.
5. Mention custom deleters (unique_ptr with a deleter for a C API handle) as the way to wrap vendor or driver APIs safely.

Follow-up

• An instrument's process grows by a few MB per hour but no leak checker reports anything. What do you check next?
• When is shared_ptr the wrong choice, and what would you use instead?
• How would you guarantee no allocation happens in a real-time acquisition thread?

Explain the implementation of a circular buffer and its use cases in real-time data ingestion.

Medium · Coding & Algorithms

Approach

1. Describe the structure: a fixed-capacity array plus a read index and a write index. Push writes at the write index and advances it, pop reads at the read index and advances it, and both wrap around. Both operations are O(1) and allocation-free, which is why the structure suits real-time ingestion.
2. Resolve the full-versus-empty ambiguity explicitly. Keep a count, leave one slot unused, or use monotonically increasing unsigned counters where size equals write minus read and the index is the counter masked with capacity minus one. The last option needs a power-of-two capacity, so masking replaces modulo and counter overflow stays correct.
3. State an overflow policy, because it depends on the data. Overwriting the oldest entry suits telemetry or keeping the last N log lines. Blocking or rejecting new entries suits measurement data that must not be lost. Say which one an instrument data stream needs and why.
4. For concurrency, give the single-producer single-consumer lock-free version. The producer writes the slot, then publishes the write index with a release store. The consumer reads the write index with an acquire load before reading the slot. Each side writes only its own index, and padding the two indices onto separate cache lines avoids false sharing.
5. Cover edge cases: capacity of zero or one, reading a batch that spans the wrap point (two memcpy calls), and the use cases: sensor or detector streams, audio, network receive rings, and log retention.

Follow-up

• How does the design change with multiple producers?
• A consumer wants to read 4,000 contiguous bytes but the data wraps around the end of the array. How do you hand it over without an extra copy?
• Why might a mutex-based version be the better engineering choice even though a lock-free one exists?

Given a set of constraints, how would you optimize an algorithm to reduce time complexity?

Medium · Coding & Algorithms

Approach

1. Turn the constraints into a target first. For example, n up to 10^5 rules out O(n^2) and points to O(n log n) or O(n). Tiny n with exponential choices points to backtracking or bitmask DP. State the target out loud before you optimise.
2. Name the brute force and find the repeated work in it, then match it to a technique: repeated lookups become a hash map, pair or range searches become sorting plus two pointers or binary search, repeated range sums become prefix sums, contiguous-window questions become a sliding window, top-k becomes a heap of size k, and overlapping subproblems become memoization or tabulated DP.
3. Say what you trade for the speed: usually memory (a hash index or DP table), sometimes preprocessing time that only pays off over many queries. Check that the memory still fits.
4. Check correctness by comparing the optimised version against the brute force on small random inputs, and list edge cases: empty input, duplicates, all-equal values, sorted or reverse-sorted input, and integer overflow in sums.
5. Name worst cases honestly. Hashing has adversarial collision cases, and quickselect is O(n^2) in the worst case without random pivots.

Follow-up

• Memory is now capped well below your hash index's size. What do you give up?
• The input becomes a stream you can only read once. Which of these techniques still work?
• How would you confirm that the optimisation matters on real data, not just in asymptotic terms?

How do you approach debugging a race condition in a multi-threaded application?

Medium · Coding & Algorithms

Approach

1. Reproduce first and make it happen more often: run a stress loop for thousands of iterations, use more threads than cores, insert yields or sleeps at suspected points, and run on a machine with more cores. A race that shows up once a day is very hard to fix with confidence.
2. Use tools before guessing: ThreadSanitizer (compile with -fsanitize=thread), Helgrind, or a debugger with thread-aware breakpoints. Treat logging with care, because it changes timing and can hide the bug. Prefer lock-free trace buffers with thread IDs and sequence numbers.
3. Find the shared mutable state and the invariant that breaks. Common shapes are check-then-act (if not initialised, then initialise), read-modify-write (count++), and publishing an object before it is fully built. In C++, volatile gives neither atomicity nor ordering. In Python, += on a shared value is not atomic despite the GIL.
4. Fix it with the smallest correct change: a mutex covering the whole invariant rather than each field, std::atomic for a single word, or removing sharing altogether through message passing or per-thread ownership. Check lock ordering so the fix does not create a deadlock.
5. Prove the fix: run the same stress loop with no failures and a clean sanitizer report, and add a regression test that ran the racy path under load.

Follow-up

• The fix you shipped now occasionally deadlocks. How do you find the lock-order inversion?
• The race only reproduces on the instrument, never on your workstation. What do you change about your approach?
• When would you choose an atomic over a mutex here, and what could go wrong?

Denormalise tenant onto revisions and backfill it live

Hard · denormalisation · online migration · backfill

resource_revision (revision_id, resource_id, version, actor_user_id, change_kind, patch, request_id, created_at) has 400M rows and no tenant column; tenant_id lives only on resource. Two reads need it: a tenant-scoped audit feed ordered by created_at DESC, and an offboarding purge. Both join back to resource today. Justify adding tenant_id to resource_revision against those two reads, name the anomaly the copy introduces and the constraint that prevents it, then give the ordered migration for a live table taking 1.2k writes/second — the lock each step takes, how the backfill is batched, and where each step stops being reversible. PostgreSQL 16.

Approach

1. Justify from the access path rather than from taste. Without the column, the audit feed either scans resource_revision by created_at and discards other tenants' rows, or resolves the tenant's resource_ids first and probes with them — both proportional to the tenant's whole history rather than to one page. With (tenant_id, created_at DESC, revision_id DESC) it is a seek that stops at 50 rows, and the purge becomes a ranged delete instead of a join.
2. Name the cost exactly: a second copy of a fact can disagree with the first. Make the disagreement unwritable rather than documented — add UNIQUE (resource_id, tenant_id) on resource so it can serve as a foreign-key target, then FOREIGN KEY (resource_id, tenant_id) REFERENCES resource (resource_id, tenant_id) on the revision table. A revision can then only ever carry its parent's tenant.
3. Step one, expand: ALTER TABLE resource_revision ADD COLUMN tenant_id BIGINT NULL, with no default, so it is a catalogue change and no rewrite. It still needs ACCESS EXCLUSIVE for an instant, and that instant queues behind the longest open transaction on the table while every later query queues behind it — set lock_timeout to 2s and retry rather than wait.
4. Step two, dual-write: deploy the writer that populates tenant_id on every new revision while reads still use the join. Reversible by redeploying the previous build, because nothing reads the column yet.

Follow-up

• The backfill is half finished and a rollback is required. What state is the table in, and what does the previous build do with a half-populated column?
• How do you verify the backfill actually finished, given rows are still being inserted while it runs?

Stop tag and share joins from fanning out a page

Medium · join fan-out · semi-joins · many-to-many

resource_tag is (resource_id, tag_id) with PK (resource_id, tag_id); resource_share is (resource_id, shared_with_user_id, permission). The tagged-and-shared listing inner-joins resource to both, filters tenant_id, tag_id = ANY($2) and shared_with_user_id = $3, orders by updated_at DESC and takes 50. Pages come back with fewer than 50 distinct resources and the total in the header is far too high. Explain the row multiplication, rewrite both the page query and the count query so each is correct, and name the index each one needs. PostgreSQL 16.

Approach

1. Do the arithmetic against the predicates that are actually there. An inner join emits one row per matching child row, and both joins are filtered: tag_id = ANY($2) admits only the requested tags, shared_with_user_id = $3 admits one user's share rows. So a resource holding three of the requested tags and shared with $3 once yields three rows, not one — the multiplier is its count of matching tags times its share rows for that single user, and that second factor is 1 unless the table admits duplicate (resource_id, shared_with_user_id) pairs. LIMIT 50 then limits rows rather than resources, and COUNT(*) counts pairs — the header is the product, not the population.
2. Reject DISTINCT as the fix. It deduplicates after the product has been built, so the planner must materialise and sort the fanned-out set before the LIMIT can apply, and it leaves any SUM or AVG in the same select list wrong.
3. Rewrite both filters as semi-joins, keeping resource as the only row source: AND EXISTS (SELECT 1 FROM resource_tag rt WHERE rt.resource_id = r.resource_id AND rt.tag_id = ANY($2)) and the same shape against resource_share. A semi-join stops at the first match per resource and preserves the driving index order, so ORDER BY updated_at DESC, resource_id DESC LIMIT 50 still stops after 50 rows.
4. Count with the same predicates and no join at all: SELECT count(*) FROM resource r WHERE r.tenant_id = $1 AND r.status = 'active' AND EXISTS (...) AND EXISTS (...). Nothing multiplies a resource, so the number is the population.

Follow-up

• The filter changes from 'any of these tags' to 'all of these tags'. Rewrite it and state what it costs relative to the ANY form.
• A resource can be shared with the same user twice under different permissions. Does your count change, and should it?

Publish rate-limit and deadline semantics the edge actually enforces

Easy · rate limiting · backpressure · retries

The edge API serves about 3k requests/second steady and 9k at peak against a 400 ms p99 budget, with an explicit bounded concurrency limit per instance. Limits exist per principal and per tenant. Callers are a partner integration running nightly bulk loads and a browser app. Specify the counting algorithm and window, which limit a request is charged against, the headers a well-behaved client reads, the status and body when a limit is hit, how that differs from the response when an instance is shedding load, and what each caller does with each.

Approach

1. Choose the counter and name its failure mode. Fixed windows admit nearly twice the limit across a boundary - a full burst at the end of one window and another at the start of the next. A token bucket states sustained rate and burst separately, which is exactly what a nightly bulk load needs. A sliding-window counter is more faithful and costs more state per key. State the choice and the burst it permits.
2. Charge each request against both keys and reject on the stricter. The tenant limit protects the shared primary, which absorbs roughly 1.2k writes/second in total; the per-principal limit stops one credential inside a tenant from consuming that tenant's whole allowance. The tenant is the fairness unit for the same reason it is the leading column of every index.
3. Advertise limit, remaining and reset for the binding key on every response, not only on rejections, so a client can pace before it is refused. Pick one naming scheme - the RateLimit-* draft fields or an X-prefixed set - document the units, and never change them afterwards.
4. Separate two rejections that look identical to a naive client. 429 means this caller exceeded its own share and Retry-After is a real schedule it should obey. 503 means the instance is at its concurrency bound and shedding, which is a statement about the server; a fleet-wide 503 retried on a fixed delay resynchronises every client into one stampede, so full jitter is mandatory there and the delay is the client's guess, not ours.

Follow-up

• One tenant stays under its limit and still degrades everyone else during a backfill. What changes - the limiter, the worker concurrency caps, or both?
• How are counters kept correct across 20 to 40 stateless instances, and what does your answer cost per request?

Keep one unresponsive destination from stalling all webhook delivery

Medium · fan-out · circuit breaker · timeouts

Egress delivery sends about 1.5k webhooks/second to 40k destinations, with a per-destination concurrency cap of 4 and a 10-second connect-plus-read timeout. One destination begins accepting connections and never responding; within the hour 150 destinations behave the same way. Design the delivery path so unrelated destinations are unaffected: the pool structure, the timeouts, the retry policy, the per-destination circuit, and what is recorded so a retry is not a second effect at the receiver. State how many in-flight slots the degraded destinations hold and why that number decides the design.

Approach

1. Start with the number, and with the law that produces it. In-flight work is arrival rate times time in service, so 1.5k/second against a healthy 200 ms response needs about 300 concurrent slots. Per destination the same product applies, ceilinged by the concurrency cap: at the fleet average of 0.0375 deliveries/second per destination (1.5k spread over 40k) a 10-second timeout is 0.375 slots. A destination that has queued retries behind it is a different regime - every slot refills the instant an attempt expires, so it sits pinned at its cap of 4 - and 150 of those hold 600 slots, more than a pool sized for healthy traffic, entirely consumed by endpoints that will never answer. The per-destination cap bounds one endpoint and says nothing about the aggregate, which is exactly why it alone is not containment.
2. Contain with bulkheads and an admission bound rather than a larger pool. Cap total in-flight per pool and shard destinations across pools by a hash of destination id, so a correlated group - one provider, one region - cannot exceed its pool's share. A delivery refused admission and re-queued with backoff is strictly better than one holding a slot on behalf of a receiver that is not listening.
3. Treat the timeout as two timeouts, and be exact about what shortening one buys. Connect and read are separate failures and both must be shorter than the budget of whatever is waiting. Occupancy is min(cap, arrival rate x timeout), so dropping the read ceiling from 10 seconds to 3 cuts a merely slow destination's occupancy proportionally, 0.375 slots to 0.11 at the fleet average. It does not cut the 4 slots held by one of the 150: a destination with a retry backlog arrives far above cap/timeout - 0.4/second at a 10-second timeout, 1.33/second at 3 - so it stays pinned at the cap either way and only the slot-seconds per attempt fall. What that does buy is detection rate: 3.3x more failures observed per second on the same four slots, which is how fast the circuit reaches its threshold. Pick the value from the measured latency distribution of successful deliveries, with their high percentile as the floor, not from a round number.
4. Add a circuit per destination, counting a timeout as a failure. Once open, fail fast without taking a slot - that is the whole point, converting 4 held slots into zero. Half-open on a schedule with exactly one probe and close only if the probe succeeds, so a permanently dead endpoint costs one request per interval instead of a growing retry queue.

Follow-up

• The destination is not dead - it answers in 9.5 seconds with a 200. Does a failure-rate circuit open? Should anything shed that traffic, and on what signal?
• One destination requires deliveries in order. What does a per-destination concurrency of 4 do to that guarantee, and what would you change to offer it?

How would you design a robust data pipeline to handle intermittent connectivity or hardware errors?

Medium · Technical & Domain Expertise

Approach

1. Start with durability at the source. The instrument side writes data to a local append-only spool (a write-ahead log) and fsyncs before it acknowledges a chunk as captured. A network drop then delays delivery without losing data.
2. Make delivery safe to repeat. Split the data into chunks keyed by run ID plus chunk index, attach a checksum to each, and upload with retries using exponential backoff and jitter. The receiver treats a repeated key with a matching checksum as a no-op. That gives at-least-once delivery that behaves like exactly-once.
3. Separate transient errors (timeouts, connection resets) from permanent ones (checksum mismatch, malformed data, hardware fault flags). Retry the first kind. Quarantine the second in a dead-letter area with enough context to diagnose it, and never drop it silently.
4. Handle hardware errors explicitly: validate at capture, mark runs as partial or suspect instead of passing bad data downstream, and make resumption pick up from the last acknowledged chunk.
5. Cover operations: monitor spool depth, upload lag and retry counts, alert on growth, and decide in advance what happens when the local disk fills. Choose between pausing acquisition and evicting data that has already been acknowledged, and explain the trade-off.

Follow-up

• A long outage fills the instrument's local disk. What should the system do, and who decides?
• The same chunk arrives twice with different checksums. What does that tell you, and how does the receiver respond?
• How would you prove end-to-end that every byte captured reached storage intact?

Suggested practice checklist

Each day targets one cluster of the reported questions or one reported round. The order runs from finding out your team's stack, through concurrency, memory and coding, to design, the presentation and behavioral stories.

Day 1 — Prepare the recruiter screen and choose your stack

• Write the questions for the recruiter: team, C++ or Python, whether React is in scope, live-coding format, whether a presentation is expected, and location.
• Choose two past projects and write half a page on each: architecture, threading model, your decisions and the measurements behind them.
• Rehearse a short walkthrough of your background that opens with your concurrent, embedded, distributed or data-pipeline experience.

Deliverable: A recruiter question list, two project write-ups and a rehearsed background walkthrough.

Day 2 — Threads, throughput and races

• Write a staged pipeline with bounded queues and a worker pool that keeps output in input order using sequence numbers, in C++ or Python.
• Write a deliberately racy check-then-act or shared counter, catch it with ThreadSanitizer (or show lost updates in Python), then fix it and rerun under stress.
• Write answer outlines for the multithreading question and the race-condition question, including one real number from your own past work.

Deliverable: A working ordered pipeline, a racy program with its sanitizer report and fix, and two answer outlines.

Day 3 — Memory ownership and the ring buffer

• Implement a ring buffer with a power-of-two capacity, both overflow policies, and tests that cross the wrap point.
• Turn it into a single-producer single-consumer lock-free queue using acquire/release atomics, and explain the ordering in comments.
• Wrap a C-style handle in unique_ptr with a custom deleter, and write a short note on the costs of shared_ptr and on breaking cycles with weak_ptr.

Deliverable: A tested ring buffer, its lock-free variant, an RAII wrapper and a note on smart-pointer costs.

Day 4 — Algorithmic and live coding

• Solve four problems where the brute force is quadratic or worse and the fix is a hash map, sort plus two pointers, prefix sums or a heap. For each, write the constraint that rules out the brute force.
• Do one session out loud in a shared editor or on a recording: constraints and complexity first, then code, then edge cases.
• Check each solution against the brute force on small random inputs.

Deliverable: Four solved problems with complexity notes, one recorded live session, and a randomized check script.

Day 5 — Fault-tolerant pipelines and system design

• Outline an instrument-to-storage upload design: local spool, chunk keys and checksums, backoff with jitter, dead-letter handling and a disk-full policy.
• Outline a design for logging and processing terabytes of sequencing data, covering storage layout, persistence choices and how the processing stages talk to each other.
• Work through the practice question on keeping one unresponsive destination from stalling all webhook delivery, and compare its isolation ideas with your upload design.

Deliverable: Two design outlines with failure handling written out, plus notes from the webhook isolation practice question.

Day 6 — Project presentation and React refresher

• Build the project presentation around one system you owned: problem, decisions, rejected alternatives, measured results, and your own contribution.
• Give it out loud once, then cut anything that does not support a decision you made.
• If the recruiter confirmed front-end scope, build a small React component with state, an effect that fetches data, and a keyed list. Otherwise review hooks and rendering basics.

Deliverable: A rehearsed presentation and a small working React component or a page of React review notes.

Day 7 — Behavioral stories and a full run-through

• Write STAR stories for the trade-off explanation, ambiguous requirements and delivery-versus-quality prompts, each with a measurable outcome.
• Add a story about a refactor you did to improve performance, with before and after numbers.
• Run a mock panel: one coding problem, one concurrency explanation, the presentation, and two behavioral stories.

Deliverable: Four written STAR stories and notes from a full mock panel.

Behavioural preparation

The reported behavioral prompts are about the boundary between software and the people who depend on it: explaining a trade-off to someone outside software, working when requirements move, and balancing delivery against code quality. For this role, stories about working with scientists, hardware engineers or lab users fit best. Each story should centre on a decision you made and what it cost.

1. Describe a time you had to refactor a system to improve performance.
2. Tell me about a bug that appeared only on hardware or in production, and how you tracked it down.

Describe a time you had to explain a complex technical trade-off to a non-technical stakeholder.

Medium · Behavioral & Situational

Approach

1. Choose a decision with genuinely different options and real consequences, such as processing speed against accuracy, shipping a feature now against hardening it, or buying a tool against building it. The stakeholder should be someone outside software, for example a scientist, lab user or product owner. A domain expert counts if software is not their field.
2. Show the translation. Describe how you restated the options in their terms: turnaround time, risk to results, cost and what users would see. Mention any visual or small experiment you used, and avoid jargon.
3. Make your part clear: the recommendation you made, the information you gave so they could decide, and the decision they actually made, even if it was not your recommendation.
4. End with a measurable outcome and a reflection. The story fails if it is really about simplifying until they agreed with you, or if it talks down to the stakeholder.

Follow-up

• They chose the option you advised against. What happened next?
• How did you check that they actually understood the trade-off?
• What would you explain differently now?

How do you handle situations where requirements are ambiguous or shifting?

Medium · Behavioral & Situational

Approach

1. Use a real case where the requirements changed or were unclear, ideally one where you turned a scientific or business need into a software specification. Candidates report that this kind of translation is part of the role.
2. Describe the mechanics: writing assumptions down and confirming them with the owner, building the smallest slice that could be tested against real users or data, and isolating the parts most likely to change behind an interface or configuration.
3. Show judgement about when you pushed back and when you adapted. Cover the requirement you questioned, the evidence you used, and how you recorded the decision so the next change was cheaper.
4. Close with the result and its cost. The answer falls flat if you built for every possible future or waited for perfect clarity.

Follow-up

• A requirement changed after you shipped. What did you do with the work already done?
• How do you decide you have asked enough and should start building?
• How did you keep the team aligned when the target moved?

Tell us about a project where you had to balance short-term delivery pressure with long-term code quality.

Medium · Behavioral & Situational

Approach

1. Describe a shortcut you chose on purpose, not one that happened to you. Say what it was, what deadline or user need justified it, and what risk it carried.
2. Name your guardrails: tests around the shortcut, a feature flag or narrow scope, and a tracked follow-up with an owner and a date. Say which corners you refused to cut, such as data validation or error handling on paths that touch results.
3. Report what happened afterwards: whether and when the debt was paid back, what it cost in the meantime, and how you made the case for cleanup time.
4. Give specific outcomes: delivery date met, defects avoided or incurred, refactor effort. Two answers sink the story: claiming you never compromise, and a shortcut that was never revisited.

Follow-up

• How did you convince your manager to fund the cleanup?
• What is one shortcut you would never take on code that produces scientific results?
• Looking back, was the trade-off worth it?