As a Software Engineer at the Stellar Development Foundation (SDF), you are at the forefront of building the infrastructure for an equitable global financial system. You will contribute to the Stellar Core, the backbone of a high-scale, decentralized blockchain network that facilitates real-time asset issuance and currency conversion. Your work directly impacts how developers and financial institutions interact with the Stellar ecosystem, requiring a high degree of technical rigor and a passion for distributed systems. This role is not just about writing code; it is about engineering for scale, performance, and decentralization. You will work on complex problems such as consensus mechanisms, peer-to-peer networking, and multi-core scalability. Whether you are refactoring performance bottlenecks or authoring technical RFCs (Request for Comments), your contributions will be central to maintaining the network's resilience and driving its long-term evolution. Candidates who thrive here are those who enjoy deep technical challenges, appreciate the nuances of open-source development, and are motivated by the mission-driven nature of a non-profit foundation. You will have significant autonomy, allowing you to influence the architecture of systems that are being built for the first time in an ever-evolving space.
Initial Screening
reportedThe process begins with initial screenings to assess candidate fit.
What to demonstrate
- The process begins with initial screenings to assess candidate fit
- Depth in Consensus Algorithms / Consensus (Formal Models & Verification)
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 Assessment
reportedCandidates undergo deep-dive technical assessments focusing on coding and system design.
What to demonstrate
- Candidates undergo deep-dive technical assessments focusing on coding and system design
- Depth in Consensus Algorithms / Consensus (Formal Models & Verification)
How to prepare
- Answer aloud and timed: How do you approach debugging performance bottlenecks in a large-scale codebase?
- Answer aloud and timed: What are the primary challenges when ensuring network resilience against node failures?
Behavioral Fit
reportedEvaluation of the candidate's ability to work within a collaborative, engineering-focused culture.
What to demonstrate
- Evaluation of the candidate's ability to work within a collaborative, engineering-focused culture
- Depth in Consensus Algorithms / Consensus (Formal Models & Verification)
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 fit above and write down what you would ask to confirm before it.
PracHub editorial advice for the preparation topics above.
Prioritize the RFC process
Understand that SDF values written communication. Practice articulating your design decisions in a structured, document-based format.
Be ready for adversarial scenarios
While most interviews are collaborative, be prepared for interviewers to challenge your design assumptions to see how you handle pressure and defend your logic.
Focus on the "why
When coding, don't just solve the problem—explain your choice of data structures and how your solution impacts the overall system performance.
Stay mission-focused
Remember that SDF is a non-profit. Showing an interest in how your code creates "equitable access" will resonate well with the team.
Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.
Can you explain the trade-offs between different concurrency models in C++ or Rust?
Can you explain the trade-offs between different concurrency models in C++ or Rust?
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?
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?
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?
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?
How would you design a distributed system to handle high throughput while maintaining consensus?
How would you design a distributed system to handle high throughput while maintaining consensus?
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 are the primary challenges when ensuring network resilience against node failures?
What are the primary challenges when ensuring network resilience against node failures?
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 debugging performance bottlenecks in a large-scale codebase?
How do you approach debugging performance bottlenecks in a large-scale codebase?
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 Stellar Development Foundation candidates report.
Prepare, practise & reflect
One practical outcome each day. Spend longer where you need it.
0 / 7 done01Map the Stellar Development Foundation loop
- Write out the reported sequence: Initial Screening, Technical Assessment, Behavioral Fit.
- 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 Consensus Algorithms / Consensus (Formal Models & Verification)
- Spend the session on Consensus Algorithms / Consensus (Formal Models & Verification), which Stellar Development Foundation candidates report being tested on.
- Write one worked example in Consensus Algorithms / Consensus (Formal Models & Verification) and time yourself on it.
Deliverable: One timed worked example in Consensus Algorithms / Consensus (Formal Models & Verification).
03Work C++
- Spend the session on C++, which Stellar Development Foundation candidates report being tested on.
- Write one worked example in C++ and time yourself on it.
Deliverable: One timed worked example in C++.
04Work Distributed Systems
- Spend the session on Distributed Systems, which Stellar Development Foundation candidates report being tested on.
- Write one worked example in Distributed Systems and time yourself on it.
Deliverable: One timed worked example in Distributed Systems.
05Answer out loud: Technical & Distributed Systems
- Answer aloud, timed: How would you design a distributed system to handle high throughput while maintaining consensus?
- Answer aloud, timed: Can you explain the trade-offs between different concurrency models in C++ or Rust?
Deliverable: Spoken answers to 2 reported Technical & Distributed Systems question(s), under time.
06Answer out loud: Behavioral & Values
- Answer aloud, timed: Tell me about a time you had to advocate for a specific technical design during a code review.
- Answer aloud, timed: How do you handle feedback when your proposed architecture is challenged by peers?
Deliverable: Spoken answers to 2 reported Behavioral & Values question(s), under time.
07Dry run for Stellar Development Foundation
- Run one full mock under time, then write down the two questions you most want to ask your interviewers.
Deliverable: A completed timed mock and two questions to ask.
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.
How do you handle edge-case analysis when building mission-critical components?
How do you handle edge-case analysis when building mission-critical components?
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 advocate for a specific technical design during a code review.
Tell me about a time you had to advocate for a specific technical design 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?
How do you handle feedback when your proposed architecture is challenged by peers?
How do you handle feedback when your proposed architecture is challenged by peers?
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 balance technical debt with the need to hit a critical product milestone
Describe a situation where you had to balance technical debt with the need to hit a critical product milestone.
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 for a mission-driven non-profit like SDF?
Why are you interested in working for a mission-driven non-profit like SDF?
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
How do you handle edge-case analysis when building mission-critical components?
- 02
Tell me about a time you had to advocate for a specific technical design during a code review.
- 03
How do you handle feedback when your proposed architecture is challenged by peers?
- 04
Describe a situation where you had to balance technical debt with the need to hit a critical product milestone.
How long should I prepare for the technical interviews?
Given the rigor of the SDF process, we recommend at least 3–4 weeks of focused preparation, particularly in reviewing distributed systems theory and practicing high-performance coding in C++ or Rust.
Stellar Development Foundation Software Engineer candidate reports ↗What is the company culture like?
SDF is a mission-driven, non-profit organization. The culture is described as "smart and mature," with a strong emphasis on collaborative design and open-source values.
Stellar Development Foundation Software Engineer candidate reports ↗Is there a specific focus on blockchain knowledge?
While a deep passion for blockchain is a bonus, the core requirement is strong engineering fundamentals. If you understand distributed systems, you will be well-positioned to apply those concepts to the Stellar network.
Stellar Development Foundation Software Engineer candidate reports ↗What is the typical timeline from screen to offer?
The process can move quickly, but the depth of the interviews means it typically spans several weeks to ensure a high-quality match for both the candidate and the team.
Stellar Development Foundation Software Engineer candidate reports ↗What topics does Stellar Development Foundation test in interviews?
Stellar Development Foundation interviews most often cover Consensus Algorithms / Consensus (Formal Models & Verification), C++, Distributed Systems, Rust, and Stellar Core. The exact emphasis depends on the specific role you apply for.
Stellar Development Foundation 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.
- 01Stellar Development Foundation 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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