As a Software Engineer at Decagon, you will play a foundational role in building the leading conversational AI platform that empowers global brands to deliver concierge customer experiences. Your work directly contributes to intelligent, human-like AI agents that resolve millions of customer inquiries across chat, email, and voice channels in any language. Operating in a high-velocity environment, you will drive the systems that coordinate complex model reasoning, scale low-latency data and voice infrastructure, and shape the developer platform that allows your peers to ship with confidence. This role sits at the intersection of extreme scale, real-time distributed systems, and cutting-edge applied AI. You will contribute to critical problem spaces such as core infrastructure, data pipelines, ML serving, agent orchestration, and real-time telephony stacks. Whether you are tuning tight p95 and p99 latencies for LLM inference or architecting the execution engine that powers intelligent agents, your code will directly impact tier-one enterprise customers like Duolingo, Oura, Eventbrite, and Hertz. Expect a fast-paced, in-office culture driven by a shared commitment to excellence, momentum, and customer-centric problem solving. You will work alongside world-class engineering teams who value technical rigor, autonomy, and ownership.
Recruiter Screening
reportedInitial discussion with a recruiter about career goals, background, and alignment with the company's mission.
What to demonstrate
- Initial discussion with a recruiter about career goals, background, and alignment with the company's mission
- Depth in 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 Assessments
reportedSeries of coding rounds focusing on data structures and algorithms, threading, and language internals.
What to demonstrate
- Series of coding rounds focusing on data structures and algorithms, threading, and language internals
- Depth in Algorithms
How to prepare
- Answer aloud and timed: Implement a shopping cart system for a cooking application with methods to add recipes, remove recipes, calculate total discounts based on ingredient quantities, and support undoing and redoing cart operations.
- Answer aloud and timed: Solve challenging algorithmic problems involving basic and advanced data structures, focusing on efficiency and edge-case management.
Onsite Interview Loop
reportedComprehensive onsite interviews featuring system design and domain-specific challenges.
What to demonstrate
- Comprehensive onsite interviews featuring system design and domain-specific challenges
- Depth in Algorithms
How to prepare
- Answer aloud and timed: Write production-ready code in Python, TypeScript, or JavaScript that handles asynchronous operations cleanly.
- Answer aloud and timed: Evaluates your ability to design scalable, fault-tolerant distributed systems with clear trade-offs around latency, consistency, and availability.
3 candidate reports. Individual accounts describe a particular role and hiring cycle.
Decagon Software Engineer Interview Experience — CSAT Tracker Question on the Phone Screen
I interviewed for 45 minutes and got one question. I'd solved it before, but I still wrote two bugs. Luckily I caught and fixed both while testing, and it passed in the end. Thanks to the interviewer for going easy on me. The question was the one that comes up most often for the phone screen: design a customer satisfaction (CSAT) tracking class. Scores are fixed in the range 1 to 5. __init__(self…
Read full experienceDecagon AI Engineer Interview Experience — A Tic-Tac-Toe Sequence-Counting Problem in the Coding Round
I recently interviewed for Decagon's AI Engineer role, first round (the coding round), and got a question that doesn't have an exact match on LeetCode, so I wanted to share it. Problem Two players take turns placing marks on a 3x3 board, X goes first, then O, alternating. A game is the entire sequence of moves from an empty board to the end of the game. The game ends immediately as soon as one si…
Read full experienceDecagon Software Engineer Interview Experience — Shopping Cart Versioning, Then Rejected on Tic-Tac-Toe
Round 1 was the shopping cart problem that other interview reports have also mentioned, but part 2 seemed a bit different. You're building a shopping cart for a cooking app. Users can add recipes to their cart. Each recipe has a list of ingredients (e.g., Chicken, Garlic). When the same ingredient appears across multiple recipes, the total count qualifies for bulk discounts. Part 1 was the same a…
Read full experiencePracHub editorial advice for the preparation topics above.
Going into the loop without having done this.
Communicate your assumptions proactively: Interviewers at Decagon value collaborative problem solvers who think out loud. When presented with an ambiguous prompt or coding challenge, explicitly state your assumptions, verify edge cases, and talk through trade-offs before diving into implementation.
Going into the loop without having done this.
Embrace feedback and hints: If an interviewer offers a simplification or a nudge during a coding or system design round, do not view it as a penalty. Treat it as real-time engineering collaboration, incorporate the feedback smoothly, and explain how it alters your approach.
Going into the loop without having done this.
Focus on failure modes in design: When discussing system architecture, never stop at the happy path. Actively highlight potential bottlenecks, single points of failure, and how your design handles network partitions, traffic spikes, or upstream service degradation.
Going into the loop without having done this.
Demonstrate a winner's mindset: Reflect the company's core values by showing high agency, enthusiasm for solving hard technical problems, and a commitment to operational excellence and team success.
Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.
Focuses on your core algorithmic fluency, ability to write clean code under time constraints, and aptitude for
Focuses on your core algorithmic fluency, ability to write clean code under time constraints, and aptitude for translating functional requirements into robust implementations.
Approach
- Restate the input: its shape, its size, and what is guaranteed about it.
- Name the brute-force solution and its complexity before improving on it.
- Choose the data structure from the access pattern, not from familiarity.
- 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 a shopping cart system for a cooking application with methods to add recipes, remove recipes, calcul
Implement a shopping cart system for a cooking application with methods to add recipes, remove recipes, calculate total discounts based on ingredient quantities, and support undoing and redoing cart operations.
Approach
- Restate the input: its shape, its size, and what is guaranteed about it.
- Name the brute-force solution and its complexity before improving on it.
- Choose the data structure from the access pattern, not from familiarity.
- 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 challenging algorithmic problems involving basic and advanced data structures, focusing on efficiency an
Solve challenging algorithmic problems involving basic and advanced data structures, focusing on efficiency and edge-case management.
Approach
- Restate the input: its shape, its size, and what is guaranteed about it.
- Name the brute-force solution and its complexity before improving on it.
- Choose the data structure from the access pattern, not from familiarity.
- 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?
Write production-ready code in Python, TypeScript, or JavaScript that handles asynchronous operations cleanly.
Write production-ready code in Python, TypeScript, or JavaScript that handles asynchronous operations cleanly.
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?
Tests your deep understanding of language runtimes, threading models, and low-level system performance.
Tests your deep understanding of language runtimes, threading models, and low-level system performance.
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?
Explain and implement solutions involving threading, synchronization, and race condition avoidance in concurre
Explain and implement solutions involving threading, synchronization, and race condition avoidance in concurrent environments.
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?
Describe how asynchronous programming patterns behave under high load in Python or TypeScript.
Describe how asynchronous programming patterns behave under high load in Python or TypeScript.
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?
Optimize networking paths and apply smart caching or queuing to tune CPU, memory, and I/O for tight latency pe
Optimize networking paths and apply smart caching or queuing to tune CPU, memory, and I/O for tight latency percentiles.
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?
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?
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?
Design a Customer Satisfaction (CSAT) tracking class with a fixed score range of 1 to 5, including a time wind
Design a Customer Satisfaction (CSAT) tracking class with a fixed score range of 1 to 5, including a time window size, a method to record unique conversation IDs and scores with strictly increasing timestamps, and a way to calculate average scores or specified percentiles within active windows.
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?
Evaluates your ability to design scalable, fault-tolerant distributed systems with clear trade-offs around lat
Evaluates your ability to design scalable, fault-tolerant distributed systems with clear trade-offs around latency, consistency, and availability.
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?
Design a real-time messaging application akin to Slack, detailing message routing, data storage, and connectio
Design a real-time messaging application akin to Slack, detailing message routing, data storage, and connection management.
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?
Architect a high-throughput multi-provider routing system for large language model (LLM) inference that guaran
Architect a high-throughput multi-provider routing system for large language model (LLM) inference that guarantees low latency and high availability.
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?
Design a telemetry and streaming data platform that handles customer-facing analytics and batch processing eff
Design a telemetry and streaming data platform that handles customer-facing analytics and batch processing efficiently.
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?
Discuss how to support customer-managed, air-gapped deployments with robust observability and upgrade paths.
Discuss how to support customer-managed, air-gapped deployments with robust observability and upgrade paths.
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?
p99 jumped on one listing filter while p50 stayed flat
After a release that added an owner_user_id filter to the resource listing, p99 rose from 90 ms to 1.9 s while p50 stayed at 40 ms. Traffic and row counts are unchanged. resource carries the index (tenant_id, status, updated_at DESC, resource_id DESC). The new query filters tenant_id and owner_user_id, orders by updated_at DESC, resource_id DESC, and takes 20 rows. On PostgreSQL, explain the shape of the regression, prove it from a query plan, and give the index you would add.
Approach
- Start from the shape. A flat p50 with a moved p99 means a subset of requests changed cost, not all of them, so the first job is naming the subset. Bucket the endpoint's latency by the tenant's row count; the natural hypothesis is that large tenants are a small share of requests and all of the tail.
- Get the plan for the new query on a large tenant with EXPLAIN (ANALYZE, BUFFERS). Expect an index scan over the tenant's range, a filter discarding most of it, then a Sort feeding the Limit, possibly reporting Sort Method: external merge Disk. Read actual rows on the scan node, not estimated.
- Explain why the existing index cannot serve it. A composite B-tree is seekable only as a left prefix, and with no equality predicate on status the scan cannot treat updated_at as an ordering, because rows in the tenant's range are ordered by status first. Everything matching must be read and sorted before LIMIT 20 can apply, so a tenant with 400,000 rows pays 400,000 rows to return 20.
- Add (tenant_id, owner_user_id, updated_at DESC, resource_id DESC). Equality on the first two columns leaves the index ordered by updated_at within that pair, so the plan becomes an index scan that stops after 20 rows with no Sort node. PostgreSQL can scan a B-tree backwards, so the DESC markers matter only if the two sort columns ever disagree in direction; keeping them explicit documents the order the keyset cursor depends on.
Follow-up
- The endpoint paginates with OFFSET. What does page 500 cost with your index, and what does the keyset version cost?
- How would you have caught this before release, given that a 10,000-row seed database produces the same plan shape at an unnoticeable cost?
Built from the rounds and topics Decagon candidates report.
Prepare, practise & reflect
One practical outcome each day. Spend longer where you need it.
0 / 7 done01Map the Decagon loop
- Write out the reported sequence: Recruiter Screening, Technical Assessments, Onsite Interview Loop.
- 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 Algorithms
- Spend the session on Algorithms, which Decagon candidates report being tested on.
- Write one worked example in Algorithms and time yourself on it.
Deliverable: One timed worked example in Algorithms.
03Work Data Structures
- Spend the session on Data Structures, which Decagon candidates report being tested on.
- Write one worked example in Data Structures and time yourself on it.
Deliverable: One timed worked example in Data Structures.
04Work DSA (Algorithms & Data Structures) / Logic Reasoning
- Spend the session on DSA (Algorithms & Data Structures) / Logic Reasoning, which Decagon candidates report being tested on.
- Write one worked example in DSA (Algorithms & Data Structures) / Logic Reasoning and time yourself on it.
Deliverable: One timed worked example in DSA (Algorithms & Data Structures) / Logic Reasoning.
05Answer out loud: Coding and Data Structures
- Answer aloud, timed: Focuses on your core algorithmic fluency, ability to write clean code under time constraints, and aptitude for translating functional requirements into robust implementations.
- Answer aloud, timed: Design a Customer Satisfaction (CSAT) tracking class with a fixed score range of 1 to 5, including a time window size, a method to record unique conversation IDs and scores with strictly increasing timestamps, and a way to calculate average scores or specified percentiles within active windows.
Deliverable: Spoken answers to 2 reported Coding and Data Structures question(s), under time.
06Answer out loud: System Design and Architecture
- Answer aloud, timed: Evaluates your ability to design scalable, fault-tolerant distributed systems with clear trade-offs around latency, consistency, and availability.
- Answer aloud, timed: Design a real-time messaging application akin to Slack, detailing message routing, data storage, and connection management.
Deliverable: Spoken answers to 2 reported System Design and Architecture question(s), under time.
07Answer out loud: Concurrency and Technical Fundamentals
- Answer aloud, timed: Tests your deep understanding of language runtimes, threading models, and low-level system performance.
- Answer aloud, timed: Explain and implement solutions involving threading, synchronization, and race condition avoidance in concurrent environments.
Deliverable: Spoken answers to 2 reported Concurrency and Technical Fundamentals 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.
Assesses your communication skills, teamwork, alignment with company values, and approach to ambiguity.
Assesses your communication skills, teamwork, alignment with company values, and approach to ambiguity.
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 should we hire you?
Why should we hire you?
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 owned a large technical project end-to-end and handled unexpected roadblocks.
Tell me about a time you owned a large technical project end-to-end and handled unexpected roadblocks.
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 prioritize competing engineering demands while maintaining relentless momentum?
How do you prioritize competing engineering demands while maintaining relentless momentum?
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
Assesses your communication skills, teamwork, alignment with company values, and approach to ambiguity.
- 02
Why should we hire you?
- 03
Tell me about a time you owned a large technical project end-to-end and handled unexpected roadblocks.
- 04
How do you prioritize competing engineering demands while maintaining relentless momentum?
How difficult are the technical interviews at Decagon?
The technical bar is high, and interviewers expect rigorous problem-solving and clean execution. You should expect LeetCode medium-to-hard coding problems alongside demanding system design and concurrency evaluations. Thorough preparation on fundamentals and time management during coding rounds is essential for success.
Decagon Software Engineer candidate reports ↗What is the typical interview timeline from initial screen to final offer?
The process typically moves swiftly over the course of two to four weeks, though scheduling multiple technical rounds and an onsite loop requires good coordination. Candidates should maintain open communication with their recruiter regarding scheduling pacing and any competing offers.
Decagon Software Engineer candidate reports ↗Does Decagon operate in a remote or in-office environment?
Decagon is an in-office company, anchored by a shared commitment to velocity, collaboration, and daily in-person interaction. Candidates should be prepared to work from the designated office locations, such as San Francisco or New York, where daily lunch, dinner, and snacks are provided.
Decagon Software Engineer candidate reports ↗What differentiates successful candidates in the interview loop?
Successful candidates stand out by communicating their thought process clearly, demonstrating intellectual humility when receiving hints, and designing systems with a strong emphasis on failure modes and operational simplicity. Aligning your answers with the company's core values—such as relentless momentum and customer obsession—also leaves a strong impression.
Decagon Software Engineer candidate reports ↗How should I prepare for the system design portion of the interview?
Focus on understanding how to scale distributed systems, optimize latency percentiles, and design robust telemetry and observability pipelines. Practice breaking down open-ended prompts into core requirements, data models, and scaling bottlenecks rather than relying on memorized architectures.
Decagon Software Engineer candidate reports ↗How hard is the Decagon interview?
Candidates most commonly rate Decagon interviews as medium, based on 41 reported interviews. About 68% of candidates who interview go on to receive an offer.
Decagon Software Engineer candidate reports ↗What topics does Decagon test in interviews?
Decagon interviews most often cover Large Language Models (LLMs), System Design, Algorithms, Data Structures, and Experimentation & Iterative Research Planning. The exact emphasis depends on the specific role you apply for.
Decagon Software Engineer candidate reports ↗Sources & methodology 3 sources ↗
Official role evidence, timestamped platform data and clearly labeled preparation advice.
- 01Decagon Software Engineer candidate reports ↗
Company-reported rounds, questions and FAQ.
candidate · Accessed 2026-09-22 - 02PracHub Software Engineer practice ↗
PracHub practice material, not company-reported.
platform · Accessed 2026-09-22 - 03PracHub preparation framework ↗
PracHub preparation guidance.
platform · Accessed 2026-09-22
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