A Software Engineer at Real-Time Innovations (RTI) plays a pivotal role in shaping the future of autonomous systems, robotics, and mission-critical infrastructure. As the leading provider of messaging middleware for the Industrial Internet of Things (IIoT), RTI relies on its engineers to build and maintain high-performance, distributed systems that operate under extreme constraints. You will be working with the Data Distribution Service (DDS) standard, ensuring that data moves reliably and in real-time across complex, heterogeneous networks. This role is not just about writing code; it is about system-level thinking. You will contribute to products that power everything from autonomous vehicles and medical devices to aerospace defense systems. The impact of your work is measured by reliability, low latency, and scalability. Successful engineers here are those who thrive on solving deep technical puzzles, possess a strong grasp of networking and concurrency, and are capable of bridging the gap between low-level system performance and customer-facing requirements. ##### Tip The culture at RTI is known for long employee tenure and a deep commitment to technical excellence. Be prepared to discuss your long-term interest in the company, as they value candidates who want to build a career rather than just fill a role.
Recruiter Screen
reportedInitial screening call with a recruiter to assess your fit for the role.
What to demonstrate
- Initial screening call with a recruiter to assess your fit for the role
- Depth in C programming (C)
How to prepare
- Be able to walk your CV end to end in two minutes, and say why this company specifically.
- Have your salary expectations, notice period and location constraints ready, and ask for the rest of the loop in writing.
Technical Interviews
reportedMultiple technical interviews that may include live coding, architectural discussions, or technical presentations.
What to demonstrate
- Multiple technical interviews that may include live coding, architectural discussions, or technical presentations
- Depth in C programming (C)
How to prepare
- Answer aloud and timed: These help the team understand your communication style and your approach to collaborative problem-solving. Describe a time you had to explain a complex technical concept to a non-technical stakeholder. What motivated you to apply to RTI specifically? Tell us about a project where you faced a significant technical roadblock—how did you resolve it? How do you handle feedback during a code review?
- Work C programming (C) until you can explain it without notes
Team Interaction
reportedEngagement with engineering peers and management to evaluate team fit.
What to demonstrate
- Engagement with engineering peers and management to evaluate team fit
- Depth in C programming (C)
How to prepare
- Work C programming (C) until you can explain it without notes
- Work TCP vs UDP until you can explain it without notes
PracHub editorial advice for the preparation topics above.
Prioritize clarity
When solving coding problems, prioritize explaining your logic over writing perfect syntax immediately.
Prepare for the presentation
If you are asked to give a technical presentation, choose a topic you are passionate about, but ensure it relates to the technical work at RTI.
Know your resume
Be prepared to discuss every project on your resume in detail. You will likely be asked to explain the design decisions you made.
Ask meaningful questions
Use the time at the end of the interview to ask about the team’s current technical challenges or the company’s long-term roadmap.
Don't ignore the basics
Review fundamental networking concepts and OS internals before your onsite; these are often the "differentiators" between candidates.
Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.
These questions test your proficiency in low-level programming and your understanding of how software interact
These questions test your proficiency in low-level programming and your understanding of how software interacts with hardware. Explain the difference between the heap and the stack. How do you manage memory in C to prevent leaks? How does a mutex differ from a semaphore in a multi-threaded environment? What is the difference between TCP and UDP, and how would you implement reliability on top of UDP? Explain the role of a virtual memory system in an operating system.
Approach
- Say what the runtime actually does before reasoning about the code.
- Name what is shared across threads and what owns each piece of state.
- Identify the window where an invariant is briefly untrue.
- Distinguish a value from a reference to it, and say which one you handed out.
Follow-up
- What happens if two callers reach this at the same time?
- Where could this allocate more than you expect?
These assess your analytical approach and ability to write clean, efficient code under pressure. Given a point
These assess your analytical approach and ability to write clean, efficient code under pressure. Given a pointer to a linked list, how do you detect a cycle? Write a function to reverse a string in place. Solve a standard data structure problem (e.g., array manipulation) using a live coding platform. How would you design a simple thread-safe queue?
Approach
- Say what the runtime actually does before reasoning about the code.
- Name what is shared across threads and what owns each piece of state.
- Identify the window where an invariant is briefly untrue.
- Distinguish a value from a reference to it, and say which one you handed out.
Follow-up
- What happens if two callers reach this at the same time?
- Where could this allocate more than you expect?
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?
Replace offset paging on the resource feed with keyset
resource holds resource_id, tenant_id, owner_user_id, title, body_ref, version, status ('draft','active','archived','deleted'), created_at, updated_at, deleted_at, with an index on (tenant_id, status, updated_at DESC, resource_id DESC). The listing endpoint returns active resources for one tenant, newest update first, 50 per page, today with LIMIT 50 OFFSET n. Tenants reach page 400 and rows are created while they read. Write the keyset query, define what the cursor carries and how it is encoded, and say which part of the index each predicate uses. Assume PostgreSQL 16.
Approach
- Name the two failures separately. OFFSET 20000 makes the server produce and discard 20,000 rows, so page cost grows with depth rather than with page size. Independently, any write that changes how many rows sort above the offset moves the window between two fetches, and the direction decides which anomaly you get: an insert lands at the head of updated_at DESC and pushes already-returned rows down past the boundary, so they are returned a second time; a delete above the offset, or a row whose updated_at is bumped above the cursor, pulls rows up and one is never returned at all. Nothing in the response reveals either.
- Write the seek: WHERE tenant_id = $1 AND status = 'active' AND (updated_at, resource_id) < ($2, $3) ORDER BY updated_at DESC, resource_id DESC LIMIT 50. The row-value comparison is one index range rather than a disjunction, and both columns are NOT NULL, which is what makes that comparison well defined.
- Map each predicate onto the index: tenant_id and status are equality on the leading columns, (updated_at, resource_id) is the range, and the ORDER BY matches the index order so no Sort node appears and the scan stops after 50 rows. The DESC in the definition only matters for mixed directions — a plain ascending btree on the same columns is read backwards for this query.
- Put both sort columns in the cursor and nothing the client can tamper with into another tenant: base64 of (updated_at, resource_id), validated server-side, with tenant_id taken from the principal.
Follow-up
- The client asks for 'jump to page 400'. What do you offer instead, and what does the honest version cost?
- Sort order becomes user-selectable across four columns. How many indexes is that, and which would you refuse to add?
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?
Evolve the resource contract without breaking integrations you cannot upgrade
GET /v1/resources/{id} returns status from a four-value enum ('draft','active','archived','deleted'), a numeric version, and the body inline. Consumers are a browser app you deploy and roughly 300 server-side integrations, some untouched for two years, that switch exhaustively on status and parse ids as JSON numbers. You must add a 'pending_review' status, move bodies over 256 KB to a body_ref pointer, and expose per-field change history from resource_revision. Specify the compatibility policy, the wire changes, how both generations are served, and the evidence that lets you remove the old shape.
Approach
- Write the policy first and date it: fields are added, never retyped or repurposed; consumers ignore unknown fields; an unknown enum value maps to a documented fallback; nothing is removed until telemetry shows no caller reads it. Then say the uncomfortable part out loud - v1 shipped without the unknown-value rule, so 300 running integrations have no fallback, and no server change can install one into code that is already deployed.
- That single fact forces per-request negotiation rather than a server-side default. Keep one internal model and select a serialiser from an explicit version in the request, and default a caller that sends nothing to the oldest supported version. Defaulting to the newest is the change that breaks every integration that never asked for anything, on the day you ship.
- Downgrade 'pending_review' for old callers to the nearest state they already handle, 'draft', and state the loss explicitly: those integrations cannot see review state and will treat the resource as editable. If that is unacceptable for one integration, the remedy is moving it to the new version, not a cleverer projection - there is no mapping that invents a state the client has no code for.
- Make the body change additive. Old callers keep
bodyinline; the new shape addsbody_refand a size field, and resources over the limit are served to old callers by resolving the pointer server-side or by refusing with a documented code, chosen once and published. Never repurposebodyto carry the pointer: a client that renders it shows a storage key to a user, and that failure is silent, where a missing field would have been loud. While you are here, serialise BIGINT ids as strings in the new shape - a browser parsing JSON numbers gets IEEE-754 doubles, exact only to 2^53 - and treat that as its own breaking change requiring the same negotiation, not a quiet fix.
Follow-up
- An old integration submits a status transition while the resource is really in 'pending_review'. What does the write path accept, and what does it reject?
- Two years on you want to delete the v1 serialiser. What evidence makes that safe, and who must be contacted before it happens?
Publish rate-limit and deadline semantics the edge actually enforces
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
- 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.
- 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.
- 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.
- 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?
One log partition stops advancing while the others drain
Search results for a subset of tenants are hours stale; the rest are current. The projection consumer reports lag of zero on 15 of 16 partitions and 400,000 on one. Its error rate is flat and its CPU is idle. outbox_event has no pending rows older than a second, so the relay has published everything it holds. Identify the mechanism, give the ordered checks, and state what you do in the first ten minutes versus what you change permanently.
Approach
- Read the lag distribution first. A slow consumer lags everywhere; zero on fifteen partitions and 400,000 on one is not throughput. Idle CPU on the stuck partition means the consumer is not advancing its offset at all, which points at one message it cannot get past rather than at a rate problem.
- Exonerate the producer before touching the consumer. No pending outbox rows older than a second means the relay published, so the event exists in the log. This separates never sent from sent and never applied, which are different code paths and usually different owners.
- Read the message at the stuck offset and the handler's log lines for its event_id. A flat error rate with no progress has two explanations and you must distinguish them: the handler is throwing and the retry loop is swallowing it, or the handler is blocking on something and never returning. Idle CPU with no error lines favours the second.
- Mitigate before diagnosing further. Move the offending event to a dead-letter store and commit the offset past it. Adding consumers does nothing here, because a partition is consumed by exactly one member of the group, and the blast radius is every aggregate hashed to that partition, not only the aggregate that produced the bad event.
Follow-up
- The dead-lettered event carried aggregate_version 7 and the projection had applied 6. What must the replay do differently if 8 and 9 landed in the meantime?
- How do you show staleness to the user while the partition is behind, given the API already returns the projection's watermark?
Built from the rounds and topics Real-Time Innovations (RTI) candidates report.
Prepare, practise & reflect
One practical outcome each day. Spend longer where you need it.
0 / 7 done01Map the Real-Time Innovations (RTI) loop
- Write out the reported sequence: Recruiter Screen, Technical Interviews, Team Interaction.
- 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 C programming (C)
- Spend the session on C programming (C), which Real-Time Innovations (RTI) candidates report being tested on.
- Write one worked example in C programming (C) and time yourself on it.
Deliverable: One timed worked example in C programming (C).
03Work TCP vs UDP
- Spend the session on TCP vs UDP, which Real-Time Innovations (RTI) candidates report being tested on.
- Write one worked example in TCP vs UDP and time yourself on it.
Deliverable: One timed worked example in TCP vs UDP.
04Work Problem solving / algorithmic thinking
- Spend the session on Problem solving / algorithmic thinking, which Real-Time Innovations (RTI) candidates report being tested on.
- Write one worked example in Problem solving / algorithmic thinking and time yourself on it.
Deliverable: One timed worked example in Problem solving / algorithmic thinking.
05Answer out loud: Technical Foundations (C/C++ & OS)
- Answer aloud, timed: These questions test your proficiency in low-level programming and your understanding of how software interacts with hardware. Explain the difference between the heap and the stack. How do you manage memory in C to prevent leaks? How does a mutex differ from a semaphore in a multi-threaded environment? What is the difference between TCP and UDP, and how would you implement reliability on top of UDP? Explain the role of a virtual memory system in an operating system.
Deliverable: Spoken answers to 1 reported Technical Foundations (C/C++ & OS) question(s), under time.
06Answer out loud: Problem Solving & Algorithms
- Answer aloud, timed: These assess your analytical approach and ability to write clean, efficient code under pressure. Given a pointer to a linked list, how do you detect a cycle? Write a function to reverse a string in place. Solve a standard data structure problem (e.g., array manipulation) using a live coding platform. How would you design a simple thread-safe queue?
Deliverable: Spoken answers to 1 reported Problem Solving & Algorithms question(s), under time.
07Answer out loud: Behavioral & Situational
- Answer aloud, timed: These help the team understand your communication style and your approach to collaborative problem-solving. Describe a time you had to explain a complex technical concept to a non-technical stakeholder. What motivated you to apply to RTI specifically? Tell us about a project where you faced a significant technical roadblock—how did you resolve it? How do you handle feedback during a code review?
Deliverable: Spoken answers to 1 reported Behavioral & Situational 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.
These help the team understand your communication style and your approach to collaborative problem-solving. De
These help the team understand your communication style and your approach to collaborative problem-solving. Describe a time you had to explain a complex technical concept to a non-technical stakeholder. What motivated you to apply to RTI specifically? Tell us about a project where you faced a significant technical roadblock—how did you resolve it? How do you handle feedback during a code review?
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 callers you do not own that their integration breaks
A field in a write endpoint's response must change shape. You own the endpoint; you do not own the four internal callers or the outbound webhook consumers who read it. Describe a deprecation you were responsible for: what you shipped first, how you established who was actually reading the field, the window you gave and what set its length, what you did about the consumer who never moved, and how you decided removal was safe. Name the signal you used, not the announcement you sent.
Approach
- Establish the reader set empirically rather than from a wiki of owners: per-field usage counters keyed by principal, or access logs attributed to a consumer. State the blind spot of whichever you pick, since a consumer that reads the field only on a monthly job will not appear in a week of logs.
- Ship additive first. Populate the new field alongside the old one so no reader is forced to move, which is also what keeps a rolling deploy safe, because old and new instances answer the same requests at the same time and a rollback must still find the old shape present.
- Set the window from the slowest legitimate consumer's release cadence, not from your calendar, and decide separately what to do for a consumer with no release process at all, such as an external webhook endpoint you can only email.
- Convert silence into evidence before you rely on it: a short, low-traffic removal window that makes a still-dependent consumer fail visibly and loudly while you are watching, rather than at three in the morning after you have moved on.
Follow-up
- How would you detect a consumer that reads the field only during a monthly export?
- One caller refuses to move and has a commercial relationship behind it. What changes in your plan and what does not?
Narrate an outage you owned from page to postmortem
Pick an incident you personally drove, ideally one where writes were affected rather than reads. In six to eight minutes: state the symptom as it first appeared on a dashboard, the blast radius you established before you knew the cause, the mitigation you applied and when, the mechanism you eventually proved, and the follow-up that would prevent a repeat. Bring numbers: error rate, tenants affected, minutes to mitigate, minutes to resolve. If you cannot name what you measured, choose a different incident.
Approach
- Open on the signal rather than the cause: which metric at which percentile moved, on which service, at what time, so the listener follows the same evidence you had rather than a conclusion you already reached.
- Separate mitigation from diagnosis out loud. State what you did to stop the bleeding (flag off, shed traffic, drain a lease, roll back a deploy) and say plainly that you did it before the mechanism was known, because those are two jobs with different deadlines.
- Establish blast radius in countable terms: how many tenants, how many writes, and crucially whether the effect was loss or only delay. An append-only revision table or a pending outbox row means the change survived and the projection was merely behind, which is a repair rather than a data-loss incident.
- Prove the mechanism instead of asserting it. Name the trace span that grew, the plan that flipped to a sequential scan, the lease that expired, plus one alternative you ruled out and the signal that stayed flat while you ruled it out.
Follow-up
- What would you do differently in the first five minutes, given the same dashboard and no more information?
- Which follow-up action did you deliberately not take, and why was dropping it the right call?
- 01
These help the team understand your communication style and your approach to collaborative problem-solving. Describe a time you had to explain a complex technical concept to a non-technical stakeholder. What motivated you to apply to RTI specifically? Tell us about a project where you faced a significant technical roadblock—how did you resolve it? How do you handle feedback during a code review?
- 02
A field in a write endpoint's response must change shape. You own the endpoint; you do not own the four internal callers or the outbound webhook consumers who read it. Describe a deprecation you were responsible for: what you shipped first, how you established who was actually reading the field, the window you gave and what set its length, what you did about the consumer who never moved, and how you decided removal was safe. Name the signal you used, not the announcement you sent.
- 03
Pick an incident you personally drove, ideally one where writes were affected rather than reads. In six to eight minutes: state the symptom as it first appeared on a dashboard, the blast radius you established before you knew the cause, the mitigation you applied and when, the mechanism you eventually proved, and the follow-up that would prevent a repeat. Bring numbers: error rate, tenants affected, minutes to mitigate, minutes to resolve. If you cannot name what you measured, choose a different incident.
How difficult are the technical interviews?
The difficulty is generally considered moderate to high, as they focus on deep system-level concepts rather than just "LeetCode" style puzzles. Expect to be challenged on your understanding of how code interacts with the underlying OS and network.
Real-Time Innovations (RTI) Software Engineer candidate reports ↗How long does the process take?
The process typically takes one to three months, involving several rounds of interviews. RTI is generally prompt with feedback, so you will rarely be left guessing about your status.
Real-Time Innovations (RTI) Software Engineer candidate reports ↗Is there a specific coding language I should master?
C++ is the primary language used at RTI. Being highly proficient in modern C++ standards and understanding memory management in C will be your greatest advantage.
Real-Time Innovations (RTI) Software Engineer candidate reports ↗Does RTI offer remote work?
Policies vary by location and team. Be sure to clarify the expectations regarding office attendance or remote flexibility during your initial recruiter screen.
Real-Time Innovations (RTI) Software Engineer candidate reports ↗What topics does Real-Time Innovations (RTI) test in interviews?
Real-Time Innovations (RTI) interviews most often cover Concurrency, Distributed Systems, Marketing Analytics, C programming (C), and Physical AI. The exact emphasis depends on the specific role you apply for.
Real-Time Innovations (RTI) 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.
- 01Real-Time Innovations (RTI) 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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