As a Software Engineer at Sedaro, you are at the forefront of digital mission engineering. You will be tasked with architecting and refining high-performance simulation runtimes and workflow orchestration systems that allow aerospace teams to execute massive, complex optimization studies. The work you do directly impacts how the next generation of satellite constellations and defense systems are designed, tested, and deployed. This role is inherently cross-functional, requiring you to bridge the gap between high-level software architecture and physical system constraints. Whether you are working on the Platform Services team to optimize multi-cloud throughput or the Simulator team to push the boundaries of parallelized physics simulations, you are expected to build "power tools" that multiply the capabilities of other engineers. You will thrive here if you enjoy navigating technical ambiguity and prefer rapid, impactful iteration over exhaustive, long-term planning. 02 · Compensation
Screening Conversation
reportedInitial conversation to assess candidate fit and discuss the role.
What to demonstrate
- Initial conversation to assess candidate fit and discuss the role
- Depth in Modeling & Simulation
How to prepare
- Answer aloud and timed: How would you architect a workflow orchestration system to handle massive, non-linear simulation campaigns?
- Answer aloud and timed: Explain the trade-offs you have encountered when choosing between different relational and key-value database models for high-throughput data.
Take-Home Project
reportedA substantial project that reflects real-world challenges, requiring significant time commitment.
What to demonstrate
- A substantial project that reflects real-world challenges, requiring significant time commitment
- Depth in Modeling & Simulation
How to prepare
- Answer aloud and timed: How do you approach profiling and optimizing performance in a distributed, multi-physics simulation environment?
- Answer aloud and timed: What is your experience with gRPC and managing service-to-service communication in a cloud-native environment?
Technical Deep Dives
reportedIn-depth technical discussions with engineering peers and leadership.
What to demonstrate
- In-depth technical discussions with engineering peers and leadership
- Depth in Modeling & Simulation
How to prepare
- Answer aloud and timed: How would you design a system to monitor and alert on SLOs in a complex, multi-cloud environment?
- Answer aloud and timed: Walk me through a time you led a root cause analysis for a critical system failure. What were the steps to resolution and prevention?
Final Round Discussion
reportedOnsite or final discussion evaluating technical vision and alignment with the company’s mission.
What to demonstrate
- Onsite or final discussion evaluating technical vision and alignment with the company’s mission
- Depth in Modeling & Simulation
How to prepare
- Answer aloud and timed: Given the constraints of an autonomous edge device, how would you adapt a simulation runtime designed for the cloud?
- Answer aloud and timed: Why are you interested in the intersection of software engineering and aerospace physical systems?
PracHub editorial advice for the preparation topics above.
Show your work
When discussing past projects, clearly articulate the trade-offs you made. Sedaro interviewers appreciate engineers who understand that every choice has a cost.
Ask meaningful questions
Use your time with the CTO and managers to ask about the long-term technical roadmap. It shows you are thinking like an owner.
Prepare for the "Why
Be ready to explain why you want to work at a startup in the aerospace space. They are looking for people committed to the mission, not just a job.
If you have multiple offers, be prepared to share that context
The leadership team values high-caliber candidates who are in demand.
Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.
Given the constraints of an autonomous edge device, how would you adapt a simulation runtime designed for the
Given the constraints of an autonomous edge device, how would you adapt a simulation runtime designed for the cloud?
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?
Diff a projection against the primary without per-row point reads
The listing projection has drifted and some rows show a stale version. The primary holds 40,000,000 resource rows across 12,000 tenants while serving 1,200 writes and 14,000 reads per second. The obvious repair, reading each resource row and comparing its version against the projection, is correct and would eventually finish. Explain precisely why it is unacceptable here, then give a diff that finds the differing rows, state its complexity, and make it safe to run against a live primary. Replication lag is usually under 100 ms and is not bounded.
Approach
- Quantify the naive cost rather than calling it slow: 40,000,000 point reads at even 0.5 ms each is over five hours serialised, and the only lever is concurrency, which is exactly what you cannot spend. The primary's pool is sized for the write path, and 40,000,000 random reads evict the buffer cache that sustains the 85 percent cache hit rate, so the audit degrades the system it is auditing.
- Replace random access with one ordered pass per side. Both sides can be read in (tenant_id, resource_id) order, which is a sequential scan on each and a merge join in O(n) time and O(1) memory. For a dense diff that is the whole answer, and it reads the primary once instead of 40,000,000 times.
- For the expected sparse case, compare range hashes instead of rows: partition the key space, compute per range an order-independent aggregate over hash(resource_id, version), compare aggregates, and descend only into ranges that differ. With d differing rows and branching factor B, at most d ranges mismatch per level, so the drill-down examines O(d log_B(n/d)) ranges and reads full rows only in mismatching leaves.
- Aggregate with a sum modulo 2^64 or a multiset hash, never XOR. XOR is order-independent but self-cancelling, so two rows wrong in the same way, or a row duplicated on one side, leave the range aggregate matching and the range is declared clean.
Follow-up
- The diff reports 900 stale rows. How do you decide between patching those rows and rebuilding the projection from resource_revision?
- Same job, but the projection lives in a search index that cannot be scanned in key order. What changes?
Canonicalise a request body into a stable idempotency fingerprint
idempotency_key.request_fingerprint is a SHA-256 over the method, path and canonicalised body, and a retry whose fingerprint differs must be rejected with 422 rather than served the stored response. Write the canonicaliser. Bodies are JSON up to 256 KB nested at most 32 levels; clients vary key order, whitespace and unicode escaping, and some send 64-bit ids as JSON numbers. Produce a deterministic byte string such that semantically identical bodies match and any semantic difference does not. State your complexity and name two normalisations you refuse to perform.
Approach
- Parse once into a tree, then re-serialise under fixed rules: object keys sorted, array order preserved, one escaping convention, no insignificant whitespace. Parsing is O(n) and sorting keys is O(k log k) per object, so O(n log n) overall with O(depth) stack, and the 32-level cap is enforced during parsing because hostile nesting is how a canonicaliser becomes a stack overflow.
- Sort keys by their UTF-8 bytes and say why the obvious implementation is wrong in some runtimes: a default string comparison that orders by UTF-16 code units places surrogate pairs, meaning code points from U+10000 up, below U+E000 to U+FFFF, which is not UTF-8 byte order, so two services written in different languages disagree on the same document.
- Do not re-encode numbers through a double. IEEE-754 binary64 represents integers exactly only up to 2^53, so normalising a 19-digit id through a float changes it, and 1 against 1.0 cannot be reconciled without deciding whether they are the same value. Preserve the literal token, and require ids as strings at the API boundary if you want them comparable.
- Reject duplicate keys rather than picking one. JSON permits them and parsers disagree, most keeping the last, so any choice you make ties the fingerprint to a parser detail that the code handling the request does not necessarily share.
Follow-up
- A client sends the same logical request with an extra field your API ignores. Same key, different fingerprint, so you return 422. Is that the right answer?
- Where does the fingerprint get computed relative to request decompression and the body-size limit?
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?
Keep soft-deleted accounts from blocking re-registration
app_user holds user_id, tenant_id, email CITEXT, password_hash (NULL for SSO principals), email_verified_at, auth_version, status ('invited','active','suspended','deactivated'), created_at, updated_at, deleted_at. Two live accounts for one address inside a tenant must be impossible, but an address freed by a soft delete must be reusable, and the same tenant may delete and re-register it repeatedly. Write the uniqueness DDL for PostgreSQL 16, then the equivalent for MySQL 8 where partial indexes do not exist, and say what each permits once three deleted rows already hold that address.
Approach
- Start from what is actually unique: not (tenant_id, email), but (tenant_id, email) among live rows. PostgreSQL says that directly — CREATE UNIQUE INDEX app_user_live_email ON app_user (tenant_id, email) WHERE deleted_at IS NULL. A full constraint over the same two columns burns the address permanently the first time someone deletes an account.
- Keep case-insensitivity in the type or the index, never in the application: CITEXT as given, or UNIQUE (tenant_id, lower(email)) as an expression index where the extension is unavailable. A case-sensitive unique column is exactly how two accounts for one human appear.
- For MySQL 8 the predicate has to move inside the key: add a discriminator column that is a constant 0 while the row is live and is set to user_id on delete, with UNIQUE (tenant_id, email, deleted_marker). Live rows share the constant and still collide; deleted rows differ from each other and stop colliding.
- State the NULL variant and its dependency: leaving the marker NULL for deleted rows also works, because a unique index treats NULLs as distinct — true in MySQL, and true in PostgreSQL only under the default NULLS DISTINCT, which PostgreSQL 15 lets you reverse. Check the polarity against the three existing deleted rows: constant-on-live is what preserves the collision you want, and reversing it silently admits duplicate live accounts.
Follow-up
- A deleted account re-registers with the same address the next day. Do the old resource rows follow the new user_id, and how does the API keep the two principals apart?
- How do you honour an erasure request while resource_revision.actor_user_id still references this table?
How would you architect a workflow orchestration system to handle massive, non-linear simulation campaigns?
How would you architect a workflow orchestration system to handle massive, non-linear simulation campaigns?
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 trade-offs you have encountered when choosing between different relational and key-value database
Explain the trade-offs you have encountered when choosing between different relational and key-value database models for high-throughput data.
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 do you approach profiling and optimizing performance in a distributed, multi-physics simulation environmen
How do you approach profiling and optimizing performance in a distributed, multi-physics simulation environment?
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 a system to monitor and alert on SLOs in a complex, multi-cloud environment?
How would you design a system to monitor and alert on SLOs in a complex, multi-cloud environment?
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?
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 Sedaro candidates report.
Prepare, practise & reflect
One practical outcome each day. Spend longer where you need it.
0 / 7 done01Map the Sedaro loop
- Write out the reported sequence: Screening Conversation, Take-Home Project, Technical Deep Dives, Final Round Discussion.
- 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 4 reported rounds, with the weakest marked.
02Work Modeling & Simulation
- Spend the session on Modeling & Simulation, which Sedaro candidates report being tested on.
- Write one worked example in Modeling & Simulation and time yourself on it.
Deliverable: One timed worked example in Modeling & Simulation.
03Work Rust
- Spend the session on Rust, which Sedaro candidates report being tested on.
- Write one worked example in Rust and time yourself on it.
Deliverable: One timed worked example in Rust.
04Work Distributed Multi-physics Simulation
- Spend the session on Distributed Multi-physics Simulation, which Sedaro candidates report being tested on.
- Write one worked example in Distributed Multi-physics Simulation and time yourself on it.
Deliverable: One timed worked example in Distributed Multi-physics Simulation.
05Answer out loud: Technical & Domain Expertise
- Answer aloud, timed: How would you architect a workflow orchestration system to handle massive, non-linear simulation campaigns?
- Answer aloud, timed: Explain the trade-offs you have encountered when choosing between different relational and key-value database models for high-throughput data.
Deliverable: Spoken answers to 2 reported Technical & Domain Expertise question(s), under time.
06Answer out loud: System Design & Architecture
- Answer aloud, timed: How would you design a system to monitor and alert on SLOs in a complex, multi-cloud environment?
- Answer aloud, timed: Walk me through a time you led a root cause analysis for a critical system failure. What were the steps to resolution and prevention?
Deliverable: Spoken answers to 2 reported System Design & Architecture question(s), under time.
07Answer out loud: Behavioral & Cultural Fit
- Answer aloud, timed: Why are you interested in the intersection of software engineering and aerospace physical systems?
- Answer aloud, timed: Tell me about a time you had to make a technical decision with incomplete information. How did you proceed?
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.
What is your experience with gRPC and managing service-to-service communication in a cloud-native environment?
What is your experience with gRPC and managing service-to-service communication in a cloud-native environment?
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?
Walk me through a time you led a root cause analysis for a critical system failure. What were the steps to res
Walk me through a time you led a root cause analysis for a critical system failure. What were the steps to resolution and prevention?
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 the intersection of software engineering and aerospace physical systems?
Why are you interested in the intersection of software engineering and aerospace physical systems?
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 make a technical decision with incomplete information. How did you proceed?
Tell me about a time you had to make a technical decision with incomplete information. How did you proceed?
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 foster a culture of technical excellence and blameless post-mortems within your team?
How do you foster a culture of technical excellence and blameless post-mortems within your team?
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
What is your experience with gRPC and managing service-to-service communication in a cloud-native environment?
- 02
Walk me through a time you led a root cause analysis for a critical system failure. What were the steps to resolution and prevention?
- 03
Why are you interested in the intersection of software engineering and aerospace physical systems?
- 04
Tell me about a time you had to make a technical decision with incomplete information. How did you proceed?
How long should I expect the interview process to take?
While timelines can vary, the process is thorough and often takes several weeks. It is best to treat this as a long-term commitment and keep your recruiter updated on your timeline.
Sedaro Software Engineer candidate reports ↗What is the most important thing to focus on for the take-home project?
Focus on clean, maintainable code and clear documentation. The team looks for how you approach the problem and your ability to explain your design decisions as much as the final output.
Sedaro Software Engineer candidate reports ↗How can I stand out during the behavioral interviews?
Show genuine interest in the intersection of software and physical systems. The team values candidates who are excited by the "why" behind the simulations, not just the "how" of the coding.
Sedaro Software Engineer candidate reports ↗What topics does Sedaro test in interviews?
Sedaro interviews most often cover Modeling & Simulation, Rust, Distributed Multi-physics Simulation, Distributed Systems, and Simulation Runtimes Development. The exact emphasis depends on the specific role you apply for.
Sedaro 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.
- 01Sedaro 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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