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Sift Software Engineer Interview Guide

Sift Software Engineer interview guide: reported rounds, questions and how candidates describe the process.

The 60-second brief

At Sift, we are redefining how modern machines are built, tested, and operated. Our platform provides engineers with real-time observability over high-frequency telemetry, eliminating critical development bottlenecks and ensuring mission-critical reliability. Born from foundational work at SpaceX on programs like Dragon, Falcon, Starlink, and Starship, Sift is built for complex, high-precision physical systems where data scalability is non-negotiable. As a Software Engineer at Sift, you will not merely write code; you will help define our core architecture, shape our product roadmap, and influence the engineering culture of an early-stage startup. You will tackle complex technical challenges, building foundational systems that ingest, stream, and visualize massive volumes of telemetry data.

This guide is scoped to a Software Engineer candidate at Sift.

Focus areas

  • Graph Algorithms
  • System Design
  • Ticketing System Design

The Sift interview process

Sift candidates report 5 rounds over 4-6 weeks. The stages below are what candidates describe, not a published process.

Recruiter Screening

Initial discussion about your background, career goals, and alignment with the company's mission.

What to demonstrate

  • Initial discussion about your background, career goals, and alignment with the company's mission
  • Depth in Graph Algorithms

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 Screen

May consist of a take-home coding assignment or a live technical phone interview focusing on algorithms and basic system design.

What to demonstrate

  • May consist of a take-home coding assignment or a live technical phone interview focusing on algorithms and basic system design
  • Depth in Graph Algorithms

How to prepare

  • Answer aloud and timed: Implement Tarjan’s algorithm to find strongly connected components in a directed graph.
  • Answer aloud and timed: Find the K-closest points to the origin in a two-dimensional coordinate space.

Virtual Onsite Loop

Highly technical and demanding sessions lasting several hours, including advanced coding challenges and system design.

What to demonstrate

  • Highly technical and demanding sessions lasting several hours
  • Including advanced coding challenges and system design

How to prepare

  • Answer aloud and timed: Solve a complex greedy or backtracking problem under ambiguous boundary conditions.
  • Answer aloud and timed: Design a highly concurrent system to buy concert tickets (such as a high-scale ticketing platform) ensuring transactional consistency under sudden traffic spikes.

Technical Project Deep Dive

In-depth discussion of your past technical projects.

What to demonstrate

  • In-depth discussion of your past technical projects
  • Depth in Graph Algorithms

How to prepare

  • Answer aloud and timed: Describe the detailed architecture of a complex project you have previously worked on, explaining your technical choices, trade-offs, and bottlenecks.
  • Answer aloud and timed: Design a real-time data ingestion pipeline capable of processing high-frequency telemetry from thousands of IoT devices or machines.

Leadership Discussion

Conversation with a hiring manager or leadership to assess fit and expectations.

What to demonstrate

  • Conversation with a hiring manager or leadership to assess fit and expectations
  • Depth in Graph Algorithms

How to prepare

  • Answer aloud and timed: Explain the technical difference between a shallow copy and a deep copy, and when you would use each.
  • Answer aloud and timed: Solve a practical phone-screen problem testing basic probability and statistical expectations.

Where candidates lose points

Over-Communicate on Boundary Conditions

During algorithmic rounds, explicitly state your assumptions and ask clarifying questions about input sizes, memory limits, and potential edge cases. This is especially critical if your interviewer presents an ambiguous problem.

Sift's engineering culture is deeply rooted in the rigorous, high-stakes environment of aerospace engineering

Approach your system design answers with an emphasis on system safety, data integrity, and proactive monitoring.

Master Your Local IDE Navigation

For the codebase coding round, ensure you are comfortable navigating large directory structures, searching for class definitions, and running terminal commands rapidly. Speed and familiarity with your tools are major differentiators.

Highlight Pragmatic Trade-offs

When designing systems, do not just propose the most complex solution. Explain why a simpler architecture might be more appropriate for an early-stage product, and outline how you would scale it as customer demand grows.

Questions & practice

Implement a level-order traversal of a binary tree.

Medium · Algorithmic & Graph Problem Solving

Approach

  1. Restate the input: its shape, its size, and what is guaranteed about it.
  2. Name the brute-force solution and its complexity before improving on it.
  3. Choose the data structure from the access pattern, not from familiarity.
  4. State the target complexity and say which constraint rules the naive version out.

Follow-up

  • How does this change if the input no longer fits in memory?
  • What is the worst case, and how likely is it on real data?

Solve the Alien Dictionary problem to find the order of characters in an alien language using topological sort

Medium · Algorithmic & Graph Problem Solving

Solve the Alien Dictionary problem to find the order of characters in an alien language using topological sorting.

Approach

  1. Restate the input: its shape, its size, and what is guaranteed about it.
  2. Name the brute-force solution and its complexity before improving on it.
  3. Choose the data structure from the access pattern, not from familiarity.
  4. State the target complexity and say which constraint rules the naive version out.

Follow-up

  • How does this change if the input no longer fits in memory?
  • What is the worst case, and how likely is it on real data?

Implement Tarjan’s algorithm to find strongly connected components in a directed graph.

Medium · Algorithmic & Graph Problem Solving

Approach

  1. Restate the input: its shape, its size, and what is guaranteed about it.
  2. Name the brute-force solution and its complexity before improving on it.
  3. Choose the data structure from the access pattern, not from familiarity.
  4. State the target complexity and say which constraint rules the naive version out.

Follow-up

  • How does this change if the input no longer fits in memory?
  • What is the worst case, and how likely is it on real data?

Find the K-closest points to the origin in a two-dimensional coordinate space.

Medium · Algorithmic & Graph Problem Solving

Approach

  1. Restate the input: its shape, its size, and what is guaranteed about it.
  2. Name the brute-force solution and its complexity before improving on it.
  3. Choose the data structure from the access pattern, not from familiarity.
  4. State the target complexity and say which constraint rules the naive version out.

Follow-up

  • How does this change if the input no longer fits in memory?
  • What is the worst case, and how likely is it on real data?

Solve a complex greedy or backtracking problem under ambiguous boundary conditions.

Medium · Algorithmic & Graph Problem Solving

Approach

  1. Restate the input: its shape, its size, and what is guaranteed about it.
  2. Name the brute-force solution and its complexity before improving on it.
  3. Choose the data structure from the access pattern, not from familiarity.
  4. State the target complexity and say which constraint rules the naive version out.

Follow-up

  • How does this change if the input no longer fits in memory?
  • What is the worst case, and how likely is it on real data?

Explain the technical difference between a shallow copy and a deep copy, and when you would use each.

Medium · Core Software Engineering & Language Fun

Approach

  1. Say what the runtime actually does before reasoning about the code.
  2. Name what is shared across threads and what owns each piece of state.
  3. Identify the window where an invariant is briefly untrue.
  4. 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?

Solve a practical phone-screen problem testing basic probability and statistical expectations.

Medium · Core Software Engineering & Language Fun

Approach

  1. Say what the runtime actually does before reasoning about the code.
  2. Name what is shared across threads and what owns each piece of state.
  3. Identify the window where an invariant is briefly untrue.
  4. 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?

Explain how you would optimize database access patterns for real-time streaming data using relational or non-r

Medium · Core Software Engineering & Language Fun

Explain how you would optimize database access patterns for real-time streaming data using relational or non-relational time-series databases.

Approach

  1. Say what the runtime actually does before reasoning about the code.
  2. Name what is shared across threads and what owns each piece of state.
  3. Identify the window where an invariant is briefly untrue.
  4. 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?

Keep soft-deleted accounts from blocking re-registration

Easy · soft delete · unique constraints · engine differences

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

  1. 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.
  2. 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.
  3. 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.
  4. 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?

Stop tag and share joins from fanning out a page

Medium · join fan-out · semi-joins · many-to-many

resource_tag is (resource_id, tag_id) with PK (resource_id, tag_id); resource_share is (resource_id, shared_with_user_id, permission). The tagged-and-shared listing inner-joins resource to both, filters tenant_id, tag_id = ANY($2) and shared_with_user_id = $3, orders by updated_at DESC and takes 50. Pages come back with fewer than 50 distinct resources and the total in the header is far too high. Explain the row multiplication, rewrite both the page query and the count query so each is correct, and name the index each one needs. PostgreSQL 16.

Approach

  1. Do the arithmetic against the predicates that are actually there. An inner join emits one row per matching child row, and both joins are filtered: tag_id = ANY($2) admits only the requested tags, shared_with_user_id = $3 admits one user's share rows. So a resource holding three of the requested tags and shared with $3 once yields three rows, not one — the multiplier is its count of matching tags times its share rows for that single user, and that second factor is 1 unless the table admits duplicate (resource_id, shared_with_user_id) pairs. LIMIT 50 then limits rows rather than resources, and COUNT(*) counts pairs — the header is the product, not the population.
  2. Reject DISTINCT as the fix. It deduplicates after the product has been built, so the planner must materialise and sort the fanned-out set before the LIMIT can apply, and it leaves any SUM or AVG in the same select list wrong.
  3. Rewrite both filters as semi-joins, keeping resource as the only row source: AND EXISTS (SELECT 1 FROM resource_tag rt WHERE rt.resource_id = r.resource_id AND rt.tag_id = ANY($2)) and the same shape against resource_share. A semi-join stops at the first match per resource and preserves the driving index order, so ORDER BY updated_at DESC, resource_id DESC LIMIT 50 still stops after 50 rows.
  4. Count with the same predicates and no join at all: SELECT count(*) FROM resource r WHERE r.tenant_id = $1 AND r.status = 'active' AND EXISTS (...) AND EXISTS (...). Nothing multiplies a resource, so the number is the population.

Follow-up

  • The filter changes from 'any of these tags' to 'all of these tags'. Rewrite it and state what it costs relative to the ANY form.
  • A resource can be shared with the same user twice under different permissions. Does your count change, and should it?

Design a highly concurrent system to buy concert tickets (such as a high-scale ticketing platform) ensuring tr

Medium · System Design & Architecture

Design a highly concurrent system to buy concert tickets (such as a high-scale ticketing platform) ensuring transactional consistency under sudden traffic spikes.

Approach

  1. Fix the scope first: who calls this, how often, and what they do when it fails.
  2. Name the read and write paths separately; they rarely have the same bottleneck.
  3. Choose a partition key and say what query it makes expensive.
  4. State the consistency you need, and where you are willing to be stale.

Follow-up

  • What breaks first when traffic grows ten times?
  • How does this behave when that dependency is down for an hour?

Describe the detailed architecture of a complex project you have previously worked on, explaining your technic

Medium · System Design & Architecture

Describe the detailed architecture of a complex project you have previously worked on, explaining your technical choices, trade-offs, and bottlenecks.

Approach

  1. Fix the scope first: who calls this, how often, and what they do when it fails.
  2. Name the read and write paths separately; they rarely have the same bottleneck.
  3. Choose a partition key and say what query it makes expensive.
  4. 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?

Design a real-time data ingestion pipeline capable of processing high-frequency telemetry from thousands of Io

Medium · System Design & Architecture

Design a real-time data ingestion pipeline capable of processing high-frequency telemetry from thousands of IoT devices or machines.

Approach

  1. Fix the scope first: who calls this, how often, and what they do when it fails.
  2. Name the read and write paths separately; they rarely have the same bottleneck.
  3. Choose a partition key and say what query it makes expensive.
  4. State the consistency you need, and where you are willing to be stale.

Follow-up

  • What breaks first when traffic grows ten times?
  • How does this behave when that dependency is down for an hour?

Describe a situation where you had to debug a critical issue in a production distributed system. What was your

Medium · Behavioral & Culture Fit

Describe a situation where you had to debug a critical issue in a production distributed system. What was your process?

Approach

  1. Establish what changed and when, before forming any theory.
  2. Pick a bisection that eliminates candidates whichever way it turns out.
  3. Check the instrumentation before believing the symptom.
  4. 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?

Suggested practice checklist

Built from the rounds and topics Sift candidates report.

Day 1 — Map the Sift loop

  • Write out the reported sequence: Recruiter Screening, Technical Screen, Virtual Onsite Loop, Technical Project Deep Dive, Leadership 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 5 reported rounds, with the weakest marked.

Day 2 — Work Graph Algorithms

  • Spend the session on Graph Algorithms, which Sift candidates report being tested on.
  • Write one worked example in Graph Algorithms and time yourself on it.

Deliverable: One timed worked example in Graph Algorithms.

Day 3 — Work System Design

  • Spend the session on System Design, which Sift candidates report being tested on.
  • Write one worked example in System Design and time yourself on it.

Deliverable: One timed worked example in System Design.

Day 4 — Work Ticketing System Design

  • Spend the session on Ticketing System Design, which Sift candidates report being tested on.
  • Write one worked example in Ticketing System Design and time yourself on it.

Deliverable: One timed worked example in Ticketing System Design.

Day 5 — Answer out loud: Algorithmic & Graph Problem Solving

  • Answer aloud, timed: Implement a level-order traversal of a binary tree.
  • Answer aloud, timed: Solve the Alien Dictionary problem to find the order of characters in an alien language using topological sorting.

Deliverable: Spoken answers to 2 reported Algorithmic & Graph Problem Solving question(s), under time.

Day 6 — Answer out loud: System Design & Architecture

  • Answer aloud, timed: Design a highly concurrent system to buy concert tickets (such as a high-scale ticketing platform) ensuring transactional consistency under sudden traffic spikes.
  • Answer aloud, timed: Describe the detailed architecture of a complex project you have previously worked on, explaining your technical choices, trade-offs, and bottlenecks.

Deliverable: Spoken answers to 2 reported System Design & Architecture question(s), under time.

Day 7 — Answer out loud: Core Software Engineering & Language Fundamentals

  • Answer aloud, timed: Explain the technical difference between a shallow copy and a deep copy, and when you would use each.
  • Answer aloud, timed: Solve a practical phone-screen problem testing basic probability and statistical expectations.

Deliverable: Spoken answers to 2 reported Core Software Engineering & Language Fundamentals question(s), under time.

Behavioural preparation

Behavioural rounds judge the decision you made and what it cost.

Describe a time when you had to work with highly ambiguous requirements. How did you define the project bounds

Medium · Behavioral & Culture Fit

Describe a time when you had to work with highly ambiguous requirements. How did you define the project bounds and deliver results?

Approach

  1. Pick a story where you made the decision, not one where you watched it.
  2. State the situation in two sentences and spend the rest on the reasoning.
  3. Give the blast radius: what could have broken, and what you measured.
  4. 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 approach collaborating with cross-functional teams, such as hardware engineers or product managers,

Medium · Behavioral & Culture Fit

How do you approach collaborating with cross-functional teams, such as hardware engineers or product managers, to define technical roadmaps?

Approach

  1. Pick a story where you made the decision, not one where you watched it.
  2. State the situation in two sentences and spend the rest on the reasoning.
  3. Give the blast radius: what could have broken, and what you measured.
  4. 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?

Estimate work you have never done and defend the range

Hard · estimation · backfills · expand-contract

You are asked to estimate a change you have never attempted: add a column to a 100-million-row table, populate it, move reads across, and drop the old shape. Give a range with the assumptions that generate it, including batch size, the signal your backfill throttles on, and wall-clock hours, and name the three unknowns that would move the number most. Then describe a real estimate you gave under comparable ignorance: how you expressed its uncertainty, what you committed to, and how wrong you turned out to be.

Approach

  1. Decompose into independently deployable steps before estimating anything: add the column nullable, write both shapes, backfill in batches, verify, move reads, stop writing the old shape, drop it. That is four deploys spread over days, and the calendar estimate is dominated by them rather than by the loop's runtime.
  2. Do the arithmetic aloud for the part that has arithmetic in it: batch size times number of batches times per-batch duration, at a write rate the primary can absorb alongside roughly 1.2k writes per second of production traffic. The loop is throttled by replication lag and lock waits, not by how fast it can issue statements.
  3. Price the schema step by its lock rather than its statement duration. In PostgreSQL an ALTER TABLE taking ACCESS EXCLUSIVE waits for every open transaction on that table while later queries queue behind it, so a millisecond change issued during a thirty-second analytics query stalls that table for thirty seconds. Adding a nullable column with a non-volatile default avoids a rewrite from version 11; a new index wants CREATE INDEX CONCURRENTLY, which cannot run inside a transaction block and leaves an invalid index behind if it fails.
  4. Express the answer as a range whose endpoints each trace to a stated assumption, then name the cheapest experiment that collapses it, which is almost always running one real batch against the real table and multiplying.

Follow-up

  • How do you verify the backfill genuinely finished, given rows written by production traffic while it ran?
  • Where does the backfill resume from after a worker is killed mid-batch, and what makes that resume point trustworthy?