At Open Cosmos, the Software Engineer role is at the intersection of cutting-edge satellite technology and scalable software infrastructure. You are not just writing code; you are building the systems that power space missions, manage satellite constellations, and process complex data streams from orbit. This position is critical to the company’s mission of making space accessible and useful for everyone. You will contribute to high-impact projects that range from low-level flight software that must operate reliably in the harsh environment of space, to the ground-segment systems that provide telemetry and mission control. The work is inherently complex, requiring a high degree of precision, a passion for robust architecture, and the ability to thrive in a multidisciplinary environment where software meets hardware.
Initial Contact
reportedThe process begins with an initial contact to discuss the opportunity and gauge interest.
What to demonstrate
- The process begins with an initial contact to discuss the opportunity and gauge interest
- Depth in Spectrum Systems Engineering
How to prepare
- Answer aloud and timed: How do you ensure code reliability for systems where physical intervention is impossible?
- Answer aloud and timed: Describe your experience with real-time operating systems or low-level systems programming.
Technical Screening
reportedHigh-level technical screening to assess foundational knowledge and skills.
What to demonstrate
- High-level technical screening to assess foundational knowledge and skills
- Depth in Spectrum Systems Engineering
How to prepare
- Answer aloud and timed: How would you handle a memory leak in a resource-constrained environment?
- Answer aloud and timed: Explain the trade-offs between different communication protocols used in satellite data transmission.
Deep-Dive Sessions
reportedIn-depth sessions focusing on architectural design and problem-solving abilities.
What to demonstrate
- In-depth sessions focusing on architectural design and problem-solving abilities
- Depth in Spectrum Systems Engineering
How to prepare
- Answer aloud and timed: How do you design a system to handle asynchronous data streams from multiple satellite sources?
- Answer aloud and timed: What strategies do you use to ensure system redundancy and fault tolerance?
Behavioral Assessment
reportedEvaluation of behavioral competencies and teamwork through situational questions.
What to demonstrate
- Evaluation of behavioral competencies and teamwork through situational questions
- Depth in Spectrum Systems Engineering
How to prepare
- Prepare three examples from your own work, each with a decision you made and an outcome you can quantify.
- Re-read the description of the behavioral assessment above and write down what you would ask to confirm before it.
Final Decision
reportedThe final decision is made based on the overall performance throughout the process.
What to demonstrate
- The final decision is made based on the overall performance throughout the process
- Depth in Spectrum Systems Engineering
How to prepare
- Answer aloud and timed: Tell me about a time you identified a critical bug in production; how did you handle it?
- Answer aloud and timed: How do you communicate complex technical constraints to non-technical stakeholders?
PracHub editorial advice for the preparation topics above.
Think out loud
During technical sessions, narrate your thought process. This helps the interviewer understand your logic and allows them to guide you if you hit a roadblock.
Be honest about your gaps
If you don't know the answer to a specific technical question, explain how you would go about finding it. We value resourcefulness over pretending to know something you don't.
Prepare your stories
Use the STAR method (Situation, Task, Action, Result) to structure your answers for behavioral questions, ensuring you highlight your personal contributions.
Review your resume
Be prepared to discuss any technical project on your resume in great detail, including the challenges you faced and the trade-offs you made.
Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.
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?
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?
Collapse a redelivered event batch into per-aggregate high-water marks
You drain a batch of up to 5,000,000 events, each (aggregate_id BIGINT, aggregate_version INT, event_type, payload). The log guarantees order within one aggregate only; the batch merges 64 partitions, and a relay failover has redelivered a range, so an older version for an aggregate can appear after a newer one. Given a map of last_applied_version per aggregate, produce the events worth applying, at most one per (aggregate_id, version), plus the count discarded. Target O(n) time. State the memory for 2,000,000 distinct aggregates and what you do when it does not fit.
Approach
- One pass, one hash map from aggregate_id to the highest version kept, and a discard counter. An event whose version is at or below last_applied_version for its aggregate is dropped without further work, which is the whole reason the event carries its version rather than a delta. O(n) expected time, O(d) space in distinct aggregates.
- Keep the maximum, never the last occurrence. The redelivered range means the final appearance of an aggregate in the batch can be an older version than one seen earlier in the same batch, so last-wins applies stale state over newer state and the projection regresses with no error anywhere.
- Cost the memory instead of calling it large: an 8-byte key plus a 4-byte version is 12 bytes of payload, and an open-addressed table held at a 0.7 load factor costs roughly 17 bytes per entry before per-slot metadata, so 2,000,000 aggregates is tens of megabytes in a native layout and several times that in a runtime that boxes both key and value.
- If the distinct set exceeds memory, partition on hash(aggregate_id) mod P and reduce each partition independently. Every event for one aggregate hashes to the same partition, so the per-partition result is exact and the merge is concatenation rather than a second reduction.
Follow-up
- The payload is a patch rather than a snapshot, so applying only the highest version loses the intermediate changes. What changes in your reduction?
- How do you detect that version 7 arrived while version 6 was never delivered, and what should the consumer do about the gap?
Hold a per-tenant active cap against concurrent creates
A tenant on the standard plan may hold at most 50 resources with status='active'. The create handler runs SELECT count(*) FROM resource WHERE tenant_id = $1 AND status = 'active', compares to 50, then inserts. Two creates arrive 3 ms apart on different instances and the tenant lands at 51. Name the anomaly, say whether PostgreSQL 16 READ COMMITTED or REPEATABLE READ prevents it and why, then give an implementation that holds the cap at READ COMMITTED with the exact statements. Finally, say what changes when the cap is 'at most one running export per tenant' on job_run.
Approach
- Name it: write skew. The two transactions read an overlapping set and write disjoint rows, so there is no row-level conflict for the engine to detect and each commit is individually legal.
- Rule out the levels precisely. READ COMMITTED takes a fresh snapshot per statement and takes no lock on the counted rows, so both see 49. PostgreSQL's REPEATABLE READ is snapshot isolation: it removes non-repeatable reads and phantoms within the snapshot but still admits write skew, because the anomaly is not a re-read of a changed row, it is a read of a set that a concurrent transaction invalidates. Only SERIALIZABLE closes it, by tracking the read dependency and aborting one transaction with SQLSTATE 40001 — a guarantee that exists only if the application re-runs the whole transaction from the read.
- Convert the set predicate into a single-row conflict: keep tenant.active_resource_count and run UPDATE tenant SET active_resource_count = active_resource_count + 1 WHERE tenant_id = $1 AND active_resource_count < 50 in the same transaction as the INSERT. Zero affected rows is the cap, returned as 409. The row lock serialises the decision at any isolation level, and contention is bounded to one tenant's row — which is also the fair-scheduling unit, unlike a global counter that would convoy every tenant behind one row.
- State the cost you just took on: a counter is a second source of truth that can drift, so every path that changes status must adjust it inside the same transaction, and a periodic reconciliation has to exist, with resource_revision as the authority for what the count should have been.
Follow-up
- A resource moves from archived back to active. Which statements change, and what breaks if the counter update and the status change land in different transactions?
- The cap becomes plan-dependent and a plan can change mid-month. Where does the number 50 live, and who reads it?
Find version gaps and relay lag with window functions
outbox_event holds event_id, aggregate_type, aggregate_id, aggregate_version, event_type, payload, status ('pending','published','dead'), attempts, created_at, published_at. A projection is missing rows and you must decide whether the relay skipped events or the consumer dropped them. Write three queries over the last seven days: one listing every aggregate_id whose published aggregate_version sequence has a hole, one giving per-day counts with a running total, and one returning the newest published event per aggregate. For each, say where the window function is evaluated relative to WHERE and LIMIT. PostgreSQL 16.
Approach
- Gaps: compute lead(aggregate_version) OVER (PARTITION BY aggregate_id ORDER BY aggregate_version) in a subquery, then filter next_version <> aggregate_version + 1 in the outer query. Window functions are evaluated after WHERE, GROUP BY and HAVING and before the outer ORDER BY and LIMIT, so the predicate cannot sit in the same WHERE clause and PostgreSQL 16 has no QUALIFY.
- Say what the seven-day filter does to the answer: it truncates every partition, so the first row per aggregate has no predecessor inside the window and a hole spanning the boundary is invisible. Widen the window, or join to resource.version as the authority for the true maximum.
- Running total: SELECT date_trunc('day', created_at) AS d, count() AS n, sum(count()) OVER (ORDER BY date_trunc('day', created_at) ROWS UNBOUNDED PRECEDING). An aggregate inside a window call is legal because grouping runs before windowing. The grouping key is unique per row here so ROWS and RANGE agree, but write the frame anyway — over ungrouped rows with tied timestamps the default RANGE frame pulls in every peer row and the total jumps.
- Newest per aggregate: DISTINCT ON (aggregate_id) ... ORDER BY aggregate_id, aggregate_version DESC is the cheap PostgreSQL-only form when an index matches that order; row_number() OVER (PARTITION BY aggregate_id ORDER BY aggregate_version DESC) = 1 is the portable form and needs a subquery for the same evaluation-order reason as the gap query.
Follow-up
- Relay failover redelivers events. Does a duplicate break the gap query, and how would you detect one from this table alone?
- Turn the gap check into a continuous monitor rather than a query someone runs after an incident. What does it watch?
How do you ensure code reliability for systems where physical intervention is impossible?
How do you ensure code reliability for systems where physical intervention is impossible?
Approach
- Clarify what is being asked and what a complete answer contains.
- State your assumptions explicitly before working the problem.
- Say what you would check first and why it is the highest-information step.
- Work from the requirement backwards to the design.
Follow-up
- What assumption would you test first?
- How would you know your answer was wrong?
Explain the trade-offs between different communication protocols used in satellite data transmission.
Explain the trade-offs between different communication protocols used in satellite data transmission.
Approach
- Clarify what is being asked and what a complete answer contains.
- State your assumptions explicitly before working the problem.
- Say what you would check first and why it is the highest-information step.
- Work from the requirement backwards to the design.
Follow-up
- What assumption would you test first?
- How would you know your answer was wrong?
How do you design a system to handle asynchronous data streams from multiple satellite sources?
How do you design a system to handle asynchronous data streams from multiple satellite sources?
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?
What strategies do you use to ensure system redundancy and fault tolerance?
What strategies do you use to ensure system redundancy and fault tolerance?
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 the integration of software components with hardware interfaces?
How do you approach the integration of software components with hardware interfaces?
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 would you handle a memory leak in a resource-constrained environment?
How would you handle a memory leak in a resource-constrained environment?
Approach
- Establish what changed and when, before forming any theory.
- Pick a bisection that eliminates candidates whichever way it turns out.
- Check the instrumentation before believing the symptom.
- Separate the trigger from the cause; the deploy is rarely the bug.
Follow-up
- What would you look at first, and what would it rule out?
- How would you tell a cause from a coincidence here?
Built from the rounds and topics Open Cosmos candidates report.
Prepare, practise & reflect
One practical outcome each day. Spend longer where you need it.
0 / 7 done01Map the Open Cosmos loop
- Write out the reported sequence: Initial Contact, Technical Screening, Deep-Dive Sessions, Behavioral Assessment, Final Decision.
- 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 5 reported rounds, with the weakest marked.
02Work Spectrum Systems Engineering
- Spend the session on Spectrum Systems Engineering, which Open Cosmos candidates report being tested on.
- Write one worked example in Spectrum Systems Engineering and time yourself on it.
Deliverable: One timed worked example in Spectrum Systems Engineering.
03Work Thermal Systems (Thermal Engineering)
- Spend the session on Thermal Systems (Thermal Engineering), which Open Cosmos candidates report being tested on.
- Write one worked example in Thermal Systems (Thermal Engineering) and time yourself on it.
Deliverable: One timed worked example in Thermal Systems (Thermal Engineering).
04Work Flight Software Engineering
- Spend the session on Flight Software Engineering, which Open Cosmos candidates report being tested on.
- Write one worked example in Flight Software Engineering and time yourself on it.
Deliverable: One timed worked example in Flight Software Engineering.
05Answer out loud: Technical Proficiency and Domain Knowledge
- Answer aloud, timed: How do you ensure code reliability for systems where physical intervention is impossible?
- Answer aloud, timed: Describe your experience with real-time operating systems or low-level systems programming.
Deliverable: Spoken answers to 2 reported Technical Proficiency and Domain Knowledge question(s), under time.
06Answer out loud: System Design and Architecture
- Answer aloud, timed: How do you design a system to handle asynchronous data streams from multiple satellite sources?
- Answer aloud, timed: What strategies do you use to ensure system redundancy and fault tolerance?
Deliverable: Spoken answers to 2 reported System Design and Architecture question(s), under time.
07Answer out loud: Behavioral and Problem-Solving
- Answer aloud, timed: Tell me about a time you identified a critical bug in production; how did you handle it?
- Answer aloud, timed: How do you communicate complex technical constraints to non-technical stakeholders?
Deliverable: Spoken answers to 2 reported Behavioral and Problem-Solving 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 real-time operating systems or low-level systems programming.
Describe your experience with real-time operating systems or low-level systems programming.
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 you had to refactor a legacy system to improve performance or stability.
Describe a time you had to refactor a legacy system to improve performance or stability.
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 identified a critical bug in production; how did you handle it?
Tell me about a time you identified a critical bug in production; how did you handle it?
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 communicate complex technical constraints to non-technical stakeholders?
How do you communicate complex technical constraints to non-technical stakeholders?
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 situation where you had to learn a new technology or tool under a tight deadline.
Describe a situation where you had to learn a new technology or tool under a tight deadline.
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 manage technical debt when facing pressure to deliver mission-critical features?
How do you manage technical debt when facing pressure to deliver mission-critical features?
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 real-time operating systems or low-level systems programming.
- 02
Describe a time you had to refactor a legacy system to improve performance or stability.
- 03
Tell me about a time you identified a critical bug in production; how did you handle it?
- 04
How do you communicate complex technical constraints to non-technical stakeholders?
How difficult are the technical interviews?
The interviews are challenging but fair. They are designed to test your depth of knowledge and your ability to apply engineering principles to real-world scenarios rather than testing obscure trivia.
Open Cosmos Software Engineer candidate reports ↗Is there a specific culture I should prepare for?
Open Cosmos values transparency, collaboration, and a "mission-first" mentality. Candidates who show a genuine interest in the impact of their work and who communicate clearly and honestly tend to be the most successful.
Open Cosmos Software Engineer candidate reports ↗What is the timeline from screen to offer?
While timelines vary depending on the specific team and headcount needs, we aim to keep the process efficient. You can generally expect the process to span a few weeks from the initial screen to the final decision.
Open Cosmos Software Engineer candidate reports ↗What topics does Open Cosmos test in interviews?
Open Cosmos interviews most often cover Spectrum Systems Engineering, Systems Engineering, React, QA Engineering, and Data Testing. The exact emphasis depends on the specific role you apply for.
Open Cosmos 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.
- 01Open Cosmos 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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