At Opentrons, a Software Engineer plays a pivotal role in democratizing biotechnology. The company's mission is to make lab automation accessible to every scientist in the world through open-source robotics. As an engineer here, you are not just writing code for web applications; you are building the software layer that controls physical robotic liquid handlers, manages complex scientific protocols, and enables researchers to run experiments with high precision. Your work directly accelerates scientific discovery, from cancer research to vaccine development. This position sits at a unique intersection of hardware, software, and biology. You will contribute to products like the Opentrons Flex and OT-2, developing everything from user-friendly protocol designers to robust execution engines that run locally on the robots. The software team is responsible for ensuring that complex physical operations are translated into seamless, reliable, and intuitive digital workflows. Working at Opentrons requires a mindset that thrives on complexity and cross-functional collaboration. You will work alongside hardware engineers, product designers, and biologists to solve multi-disciplinary challenges. It is a highly collaborative environment filled with young, motivated minds who are passionate about open-source technology and physical-digital integration.
Introductory Call
reportedInitial conversation with a team lead or recruiter to discuss your background and interest in lab automation.
What to demonstrate
- Initial conversation with a team lead or recruiter to discuss your background and interest in lab automation
- Depth in Full-Stack Development
How to prepare
- Answer aloud and timed: How do you manage complex, nested state in a React application, especially when dealing with real-time hardware status updates?
- Answer aloud and timed: Explain the difference between client-side rendering and server-side rendering, and when you would choose one over the other for a hardware control dashboard.
Technical Evaluation
reportedIncludes a live code-pairing session and a technical discussion on system architecture and past projects.
What to demonstrate
- Includes a live code-pairing session and a technical discussion on system architecture and past projects
- Depth in Full-Stack Development
How to prepare
- Answer aloud and timed: How would you optimize a web application that needs to visualize large-scale scientific protocols and render real-time step execution?
- Answer aloud and timed: Describe your experience with TypeScript and how it helps prevent runtime errors in critical production environments.
Cultural Fit Interview
reportedFocus on values, collaboration, and working style through discussions with multiple team members.
What to demonstrate
- Focus on values, collaboration, and working style through discussions with multiple team members
- Depth in Full-Stack Development
How to prepare
- Answer aloud and timed: How would you design an API that allows a web browser to communicate reliably with a local robot via WebSockets?
- Answer aloud and timed: Describe how you would handle network latency or sudden disconnections during a live robotic run.
PracHub editorial advice for the preparation topics above.
Prepare your project stories
Be ready to walk through past projects in detail. Focus on the technical challenges you faced, the trade-offs you made, and how you collaborated with others to deliver the solution.
Brush up on React and state management
Since the user interfaces manage complex physical states, a deep understanding of state flow, hooks, and performance optimization is critical.
Showcase your open-source mindset
Opentrons is built on open-source principles. If you have contributed to open-source projects or appreciate community-driven software, make sure to highlight this during your interviews.
Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.
Archive a resource graph without breaking live references or recursing
Resources reference other resources within a tenant; for the largest tenant the reference table holds up to 2,000,000 nodes and 8,000,000 edges. Archiving a resource must archive everything reachable from it that nothing outside the set still references, refuse when a live external referrer exists, and terminate when references form cycles, which they legitimately do. Produce the archive order and the refusal list, targeting O(V+E). Say what stops the traversal crossing a tenant boundary, and why recursion is the wrong control structure at this size.
Approach
- Load the subgraph with the tenant predicate on both endpoints of the edge, not only on the side you started from. Scoping the left table alone is the classic cross-tenant leak: one mis-entered edge then pulls another tenant's resources into the traversal and, worse, into the archive.
- Traverse iteratively with an explicit stack. A 2,000,000-node graph can hold a chain deep enough to exhaust a native stack in the low tens of thousands of frames, and that failure is a process crash rather than an error you can return.
- Treat cycles as data rather than corruption: compute strongly connected components with Tarjan in O(V+E) using its own explicit stack, then condense. The condensation is a DAG, so a topological order over it gives the archive order, and every member of a component archives in one transaction because no order within a cycle is valid.
- Decide refusals with reverse edges. A candidate is archivable only if every in-edge originates inside the candidate set, so build the transpose or count in-degrees restricted to the visited set, and emit each blocked resource with the id of the external referrer, which is the only part of the answer an operator can act on.
Follow-up
- The graph is read in one query and the archive writes a minute later. What can change in between, and how do you make the write safe?
- The candidate set is 400,000 resources. Is that one transaction, and if not, what does a half-finished archive look like to a reader?
Track a rolling failure rate per destination for circuit decisions
The egress service delivers about 1,500 webhooks per second across roughly 40,000 destinations, each call bounded by a 10 second timeout. Maintain, per destination, the failure rate over the trailing 60 seconds so a caller can ask before dispatch whether the circuit should open. Attempts arrive as (destination_id, finished_at_ms, outcome). Requirement: amortised O(1) per attempt, with total memory bounded by the destination count rather than by traffic. Give the structure, its exact memory, and the rule that stops a destination with three attempts from opening a circuit.
Approach
- Name the exact-deque version and then reject it as the default. Holding timestamps and advancing a tail pointer past anything older than now minus 60 seconds is a correct two-pointer window at amortised O(1) per attempt, but its memory tracks in-window traffic, so one destination in a retry storm holds hundreds of thousands of entries while thousands of quiet destinations hold none.
- Use a ring of 60 one-second buckets per destination, each bucket a pair of counters for attempts and failures. On an attempt, advance the ring by the elapsed whole seconds, zeroing at most min(elapsed, 60) buckets, then increment the head. That is amortised O(1) with a fixed footprint per destination.
- State the footprint: 60 buckets times two 4-byte counters is 480 bytes of payload per destination, so 40,000 destinations is roughly 20 to 25 MB with per-entry overhead, bounded by the catalogue rather than by the rate. The cost is granularity, since the oldest bucket ages out in whole seconds, which is far tighter than the decision needs.
- Require a minimum sample before the circuit may open. A destination with three attempts and three failures reads as 100 percent and is not evidence; a floor of roughly 20 attempts in the window makes the ratio meaningful, and below that floor use a run of consecutive failures as the trigger instead.
Follow-up
- The fleet is 30 instances and each sees roughly a thirtieth of a destination's traffic. Where does the rate actually live, and what does a per-instance answer get wrong?
- A destination answers in 9.5 seconds and succeeds. It is not failing but it is consuming your per-destination concurrency. What signal should open the circuit here?
Identify the heaviest tenants in a five-minute window under memory pressure
The edge service handles about 3,000 requests per second across roughly 50,000 tenants, peaking near 9,000. Expose the 50 heaviest tenants by request count over the trailing five minutes so limits can be tightened before one tenant's backfill starves the fleet. You may not retain five minutes of raw records. Give the exact solution and its memory, then the bounded-memory approximation with its error stated as a formula, and say which you would ship and at what tenant cardinality that choice changes.
Approach
- Do the exact version first, because it is affordable at this cardinality: a ring of 300 one-second counters per tenant, advanced lazily, is 1,200 bytes of counters per tenant and roughly 60 to 90 MB for 50,000 tenants with overhead. Carry a running total and subtract the bucket you overwrite so a window read is O(1) rather than 300 adds.
- Extract the top 50 with a size-k min-heap over the tenant sums: O(d log k) for d tenants, against O(d log d) to sort them all. Maintaining the heap continuously instead of on query requires a tenant-to-heap-index map, because incrementing a count already inside the heap means sifting from a known position, and without that map you rebuild the heap on every request.
- State the approximation precisely rather than gesturing at sketches. Misra-Gries with m counters retains every item whose true count exceeds N/(m+1), and each retained count underestimates the truth by at most N/(m+1). With m = 1,000 and N = 900,000 requests in the window the error is roughly 900 requests, which is fine for spotting a tenant sending 50,000 and useless for ranking two tenants 200 apart.
- Say what breaks when the window slides: Misra-Gries and Space-Saving are insert-only and cannot be decremented as records age out. The workable construction is one summary per sub-window, say ten seconds, with 30 summaries merged at query time, and the merged error is the sum of the per-summary errors, so the bound degrades linearly in the number of sub-windows.
Follow-up
- The heaviest tenant is heavy because of one export job rather than user traffic. Should the limiter treat those as the same tenant?
- Two tenants sit tied at the boundary of the top 50. Does your answer flap, and does the flapping matter?
Write the update path that detects a concurrent edit
resource carries version INT NOT NULL DEFAULT 1. resource_revision holds revision_id, resource_id, version, actor_user_id, change_kind, patch JSONB, request_id, created_at with UNIQUE (resource_id, version). outbox_event holds aggregate_type, aggregate_id, aggregate_version, event_type, payload, status. A PUT carries the version the client read. Write the exact statements for the single transaction that applies the edit, records the revision and enqueues 'resource.updated', and give the handler's branch on zero affected rows. Then say what PostgreSQL 16 does under READ COMMITTED when two of these updates hit one row at once.
Approach
- One transaction, three writes, no network call inside it: UPDATE resource SET title = $3, version = version + 1, updated_at = now() WHERE resource_id = $1 AND tenant_id = $4 AND version = $2; then INSERT the resource_revision row at version $2 + 1; then INSERT the outbox_event row at the same aggregate_version. The event goes to a table rather than a broker because no transaction spans both.
- Branch on the affected-row count before doing anything else. Zero has three causes — stale version, wrong tenant, row gone — so re-read once and map to 409 carrying the current version, or 404 for an id outside the caller's tenant, which also stops the endpoint confirming that another tenant's id exists.
- State the engine behaviour instead of assuming it. Under READ COMMITTED the second UPDATE blocks on the row lock, and when the first commits PostgreSQL re-evaluates the WHERE clause against the newly committed row, so the version predicate now fails and the statement reports zero rows. Under REPEATABLE READ the identical collision raises SQLSTATE 40001 instead, so the handler must fold both shapes into one conflict response.
- Keep UNIQUE (resource_id, version) even though the predicate already serialises writers. It is what makes a lost update unwritable if any other path ever reaches the revision table, and it converts a logic bug into 23505 rather than into a silently missing history row.
Follow-up
- A client sends the version it read ten minutes ago and the resource has moved three versions. What is in your 409 so it can resolve the conflict without a full re-fetch?
- Two editors, two disjoint fields, no overlap. Does your answer still refuse the second write, and should it?
Denormalise tenant onto revisions and backfill it live
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
- 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.
- 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.
- 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.
- 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?
How do you manage complex, nested state in a React application, especially when dealing with real-time hardwar
How do you manage complex, nested state in a React application, especially when dealing with real-time hardware status updates?
Approach
- Fix the scope first: who calls this, how often, and what they do when it fails.
- Name the read and write paths separately; they rarely have the same bottleneck.
- Choose a partition key and say what query it makes expensive.
- State the consistency you need, and where you are willing to be stale.
Follow-up
- What breaks first when traffic grows ten times?
- How does this behave when that dependency is down for an hour?
Explain the difference between client-side rendering and server-side rendering, and when you would choose one
Explain the difference between client-side rendering and server-side rendering, and when you would choose one over the other for a hardware control dashboard.
Approach
- Fix the scope first: who calls this, how often, and what they do when it fails.
- Name the read and write paths separately; they rarely have the same bottleneck.
- Choose a partition key and say what query it makes expensive.
- State the consistency you need, and where you are willing to be stale.
Follow-up
- What breaks first when traffic grows ten times?
- How does this behave when that dependency is down for an hour?
How would you optimize a web application that needs to visualize large-scale scientific protocols and render r
How would you optimize a web application that needs to visualize large-scale scientific protocols and render real-time step execution?
Approach
- Fix the scope first: who calls this, how often, and what they do when it fails.
- Name the read and write paths separately; they rarely have the same bottleneck.
- Choose a partition key and say what query it makes expensive.
- State the consistency you need, and where you are willing to be stale.
Follow-up
- What breaks first when traffic grows ten times?
- How does this behave when that dependency is down for an hour?
How would you design an API that allows a web browser to communicate reliably with a local robot via WebSocket
How would you design an API that allows a web browser to communicate reliably with a local robot via WebSockets?
Approach
- Fix the scope first: who calls this, how often, and what they do when it fails.
- Name the read and write paths separately; they rarely have the same bottleneck.
- Choose a partition key and say what query it makes expensive.
- State the consistency you need, and where you are willing to be stale.
Follow-up
- What breaks first when traffic grows ten times?
- How does this behave when that dependency is down for an hour?
Describe how you would handle network latency or sudden disconnections during a live robotic run.
Describe how you would handle network latency or sudden disconnections during a live robotic run.
Approach
- Say who the caller is and what they do when the call fails halfway.
- Define the identity of a request so a retry cannot double-apply it.
- Separate accepted, pending, failed and confirmed; they are different facts.
- Design the error taxonomy before the success shape; callers branch on it.
Follow-up
- What happens if the caller retries after a timeout?
- How does a client discover it is on an old version of this contract?
How do you structure a Python backend to handle concurrent execution of labware protocols while maintaining a
How do you structure a Python backend to handle concurrent execution of labware protocols while maintaining a responsive state API?
Approach
- Say who the caller is and what they do when the call fails halfway.
- Define the identity of a request so a retry cannot double-apply it.
- Separate accepted, pending, failed and confirmed; they are different facts.
- Design the error taxonomy before the success shape; callers branch on it.
Follow-up
- What happens if the caller retries after a timeout?
- How does a client discover it is on an old version of this contract?
What strategies do you use to test software that interacts with physical hardware when you do not have the phy
What strategies do you use to test software that interacts with physical hardware when you do not have the physical device on hand?
Approach
- Say who the caller is and what they do when the call fails halfway.
- Define the identity of a request so a retry cannot double-apply it.
- Separate accepted, pending, failed and confirmed; they are different facts.
- Design the error taxonomy before the success shape; callers branch on it.
Follow-up
- What happens if the caller retries after a timeout?
- How does a client discover it is on an old version of this contract?
Exports duplicate a row range about once a week
Roughly once a week an export writes a file containing a duplicated range of rows. The affected job_run rows show attempt = 1, status = succeeded, one started_at, and a lease_owner naming a different host from the one whose logs show the job starting. Leases last 30 seconds and are heartbeated every 10 from inside the handler; lease_expires_at is computed on the worker and compared against the database's now(). Find the mechanism, and give a fix that holds even if you cannot fix the clocks.
Approach
- Start from the fact that eliminates the obvious answer. attempt = 1 means no retry was recorded, so this is not a re-run after failure; two workers ran the same row concurrently and the takeover path never touched the counter. lease_owner naming a host other than the one that started the job is the same statement from the other side.
- Enumerate the mechanisms that cause a premature takeover, then find the signal that separates them. Either the lease genuinely expired because the heartbeat did not fire, which is what happens when the heartbeat runs on the handler's own thread and the handler makes a long blocking call, or it only appeared expired because two clocks disagree, since lease_expires_at is written from the worker's clock and evaluated against the database's. The discriminator is the distribution: incidents clustered on the longest exports indict the heartbeat, incidents clustered on one host indict skew. Measure both, and measure each host's offset against the database directly.
- Read the reclaim query precisely. In PostgreSQL now() is transaction start time, not statement time, so a reclaimer holding a long transaction compares against an older timestamp than expected; clock_timestamp() is the statement-time function. This is worth ruling in or out before you redesign anything, because it changes which rows look expired.
- Remove the second clock rather than trying to synchronise it. Issue and extend the lease in the database, with lease_expires_at = now() + interval '30 seconds' in both the claim and the heartbeat, so exactly one clock is ever compared and worker skew stops mattering to this predicate.
Follow-up
- The displaced worker has already streamed half the file to object storage. What makes that side effect safe to repeat?
- You now count takeovers. What alert fires on that counter, and at what threshold?
Built from the rounds and topics Opentrons candidates report.
Prepare, practise & reflect
One practical outcome each day. Spend longer where you need it.
0 / 7 done01Map the Opentrons loop
- Write out the reported sequence: Introductory Call, Technical Evaluation, Cultural Fit Interview.
- For each round, write one sentence on what it is judging, from the description above, and mark the one you are least ready for.
Deliverable: A one-page map of the 3 reported rounds, with the weakest marked.
02Work Full-Stack Development
- Spend the session on Full-Stack Development, which Opentrons candidates report being tested on.
- Write one worked example in Full-Stack Development and time yourself on it.
Deliverable: One timed worked example in Full-Stack Development.
03Work Front-End Development
- Spend the session on Front-End Development, which Opentrons candidates report being tested on.
- Write one worked example in Front-End Development and time yourself on it.
Deliverable: One timed worked example in Front-End Development.
04Work Pair Programming
- Spend the session on Pair Programming, which Opentrons candidates report being tested on.
- Write one worked example in Pair Programming and time yourself on it.
Deliverable: One timed worked example in Pair Programming.
05Answer out loud: Technical & Frontend Architecture
- Answer aloud, timed: How do you manage complex, nested state in a React application, especially when dealing with real-time hardware status updates?
- Answer aloud, timed: Explain the difference between client-side rendering and server-side rendering, and when you would choose one over the other for a hardware control dashboard.
Deliverable: Spoken answers to 2 reported Technical & Frontend Architecture question(s), under time.
06Answer out loud: System Design & Hardware Integration
- Answer aloud, timed: How would you design an API that allows a web browser to communicate reliably with a local robot via WebSockets?
- Answer aloud, timed: Describe how you would handle network latency or sudden disconnections during a live robotic run.
Deliverable: Spoken answers to 2 reported System Design & Hardware Integration question(s), under time.
07Answer out loud: Behavioral & Cultural Fit
- Answer aloud, timed: Tell me about a time you had to work with a non-technical stakeholder, such as a biologist or hardware designer, to define software requirements.
- Answer aloud, timed: How do you handle a situation where a technical discussion with a peer reaches an impasse?
Deliverable: Spoken answers to 2 reported Behavioral & Cultural Fit question(s), under time.
Expand any day for tasks and deliverables. Your progress is saved on this device.
Behavioural rounds judge the decision you made and what it cost.
Describe your experience with TypeScript and how it helps prevent runtime errors in critical production enviro
Describe your experience with TypeScript and how it helps prevent runtime errors in critical production environments.
Approach
- Pick a story where you made the decision, not one where you watched it.
- State the situation in two sentences and spend the rest on the reasoning.
- Give the blast radius: what could have broken, and what you measured.
- 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?
Tell me about a time you had to work with a non-technical stakeholder, such as a biologist or hardware designe
Tell me about a time you had to work with a non-technical stakeholder, such as a biologist or hardware designer, to define software requirements.
Approach
- Pick a story where you made the decision, not one where you watched it.
- State the situation in two sentences and spend the rest on the reasoning.
- Give the blast radius: what could have broken, and what you measured.
- 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?
How do you handle a situation where a technical discussion with a peer reaches an impasse?
How do you handle a situation where a technical discussion with a peer reaches an impasse?
Approach
- Pick a story where you made the decision, not one where you watched it.
- State the situation in two sentences and spend the rest on the reasoning.
- Give the blast radius: what could have broken, and what you measured.
- 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?
Describe a time when you had to adapt to a sudden shift in product requirements or project direction.
Describe a time when you had to adapt to a sudden shift in product requirements or project direction.
Approach
- Pick a story where you made the decision, not one where you watched it.
- State the situation in two sentences and spend the rest on the reasoning.
- Give the blast radius: what could have broken, and what you measured.
- 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?
Why are you interested in working at the intersection of open-source software and biotechnology?
Why are you interested in working at the intersection of open-source software and biotechnology?
Approach
- Pick a story where you made the decision, not one where you watched it.
- State the situation in two sentences and spend the rest on the reasoning.
- Give the blast radius: what could have broken, and what you measured.
- 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 with TypeScript and how it helps prevent runtime errors in critical production environments.
- 02
Tell me about a time you had to work with a non-technical stakeholder, such as a biologist or hardware designer, to define software requirements.
- 03
How do you handle a situation where a technical discussion with a peer reaches an impasse?
- 04
Describe a time when you had to adapt to a sudden shift in product requirements or project direction.
How technical are the conversational interviews?
Very technical. Do not let the casual, friendly tone of the interviewers lower your guard. They are evaluating your architectural thinking, your technical depth, and your decision-making process through these open-ended discussions.
Opentrons Software Engineer candidate reports ↗Do I need a background in biology or hardware to get hired?
No, prior experience in biology or hardware is not required. However, you must show a strong curiosity and willingness to learn about these domains, as your daily work will directly interact with them.
Opentrons Software Engineer candidate reports ↗What is the work culture like at Opentrons?
The culture is collaborative, mission-driven, and energetic. The team is filled with young, passionate minds who value open-source technology, continuous learning, and cross-functional teamwork.
Opentrons Software Engineer candidate reports ↗Where are the software engineering teams located?
While Opentrons has a strong presence in Brooklyn and Long Island City, NY, they offer hybrid and remote-friendly options depending on the specific team and role requirements.
Opentrons Software Engineer candidate reports ↗What topics does Opentrons test in interviews?
Opentrons interviews most often cover Problem Solving, Time Management Under Interview Constraints, No job description / postings available, Full-Stack Development, and QA Engineering. The exact emphasis depends on the specific role you apply for.
Opentrons Software Engineer candidate reports ↗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.
- 01Opentrons Software Engineer candidate reports ↗
Company-reported rounds, questions and FAQ.
Candidate reports · Accessed 2026-09-22 - 02PracHub Software Engineer practice ↗
PracHub practice material, not company-reported.
PracHub page · Accessed 2026-09-22 - 03PracHub preparation framework ↗
PracHub preparation guidance.
PracHub page · Accessed 2026-09-22
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